Overview

Dataset statistics

Number of variables47
Number of observations1470
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory539.9 KiB
Average record size in memory376.1 B

Variable types

Numeric14
Categorical33

Alerts

Age is highly overall correlated with TotalWorkingYearsHigh correlation
MonthlyIncome is highly overall correlated with TotalWorkingYears and 3 other fieldsHigh correlation
TotalWorkingYears is highly overall correlated with Age and 4 other fieldsHigh correlation
YearsAtCompany is highly overall correlated with TotalWorkingYears and 3 other fieldsHigh correlation
YearsInCurrentRole is highly overall correlated with YearsAtCompany and 2 other fieldsHigh correlation
YearsSinceLastPromotion is highly overall correlated with YearsAtCompany and 1 other fieldsHigh correlation
YearsWithCurrManager is highly overall correlated with YearsAtCompany and 1 other fieldsHigh correlation
JobLevel is highly overall correlated with MonthlyIncome and 2 other fieldsHigh correlation
StockOptionLevel is highly overall correlated with MaritalStatus_SingleHigh correlation
BusinessTravel_Non-Travel is highly overall correlated with BusinessTravel_Travel_RarelyHigh correlation
BusinessTravel_Travel_Frequently is highly overall correlated with BusinessTravel_Travel_RarelyHigh correlation
BusinessTravel_Travel_Rarely is highly overall correlated with BusinessTravel_Non-Travel and 1 other fieldsHigh correlation
EducationField_Human Resources is highly overall correlated with JobRole_Human ResourcesHigh correlation
EducationField_Life Sciences is highly overall correlated with EducationField_MedicalHigh correlation
EducationField_Medical is highly overall correlated with EducationField_Life SciencesHigh correlation
Gender_Female is highly overall correlated with Gender_MaleHigh correlation
Gender_Male is highly overall correlated with Gender_FemaleHigh correlation
JobRole_Human Resources is highly overall correlated with EducationField_Human ResourcesHigh correlation
JobRole_Manager is highly overall correlated with MonthlyIncome and 2 other fieldsHigh correlation
JobRole_Research Director is highly overall correlated with MonthlyIncomeHigh correlation
MaritalStatus_Married is highly overall correlated with MaritalStatus_SingleHigh correlation
MaritalStatus_Single is highly overall correlated with StockOptionLevel and 1 other fieldsHigh correlation
OverTime_No is highly overall correlated with OverTime_YesHigh correlation
OverTime_Yes is highly overall correlated with OverTime_NoHigh correlation
BusinessTravel_Non-Travel is highly imbalanced (52.5%)Imbalance
EducationField_Human Resources is highly imbalanced (86.8%)Imbalance
EducationField_Marketing is highly imbalanced (50.6%)Imbalance
EducationField_Other is highly imbalanced (69.0%)Imbalance
EducationField_Technical Degree is highly imbalanced (56.4%)Imbalance
JobRole_Healthcare Representative is highly imbalanced (56.6%)Imbalance
JobRole_Human Resources is highly imbalanced (77.9%)Imbalance
JobRole_Manager is highly imbalanced (63.6%)Imbalance
JobRole_Manufacturing Director is highly imbalanced (53.5%)Imbalance
JobRole_Research Director is highly imbalanced (69.5%)Imbalance
JobRole_Sales Representative is highly imbalanced (68.7%)Imbalance

Reproduction

Analysis started2023-05-21 19:29:03.831542
Analysis finished2023-05-21 19:29:32.817814
Duration28.99 seconds
Software versionydata-profiling vv4.1.2
Download configurationconfig.json

Variables

Age
Real number (ℝ)

Distinct43
Distinct (%)2.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-2.7793338 × 10-17
Minimum-2.0721922
Maximum2.5268856
Zeros0
Zeros (%)0.0%
Negative798
Negative (%)54.3%
Memory size11.6 KiB
2023-05-21T21:29:32.895561image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum-2.0721922
5-th percentile-1.4151811
Q1-0.75816996
median-0.10115885
Q30.66535411
95-th percentile1.8698745
Maximum2.5268856
Range4.5990778
Interquartile range (IQR)1.4235241

Descriptive statistics

Standard deviation1.0003403
Coefficient of variation (CV)-3.5992089 × 1016
Kurtosis-0.40414514
Mean-2.7793338 × 10-17
Median Absolute Deviation (MAD)0.65701111
Skewness0.4132863
Sum-2.6645353 × 10-14
Variance1.0006807
MonotonicityNot monotonic
2023-05-21T21:29:33.008693image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=43)
ValueCountFrequency (%)
-0.2106607045 78
 
5.3%
-0.3201625558 77
 
5.2%
-0.1011588531 69
 
4.7%
-0.6486681098 69
 
4.7%
-0.8676718125 68
 
4.6%
-0.5391662585 61
 
4.1%
-0.7581699611 60
 
4.1%
-0.4296644071 58
 
3.9%
0.1178448495 58
 
3.9%
0.3368485522 57
 
3.9%
Other values (33) 815
55.4%
ValueCountFrequency (%)
-2.072192177 8
 
0.5%
-1.962690326 9
 
0.6%
-1.853188474 11
 
0.7%
-1.743686623 13
 
0.9%
-1.634184772 16
 
1.1%
-1.52468292 14
 
1.0%
-1.415181069 26
1.8%
-1.305679218 26
1.8%
-1.196177366 39
2.7%
-1.086675515 48
3.3%
ValueCountFrequency (%)
2.526885579 5
 
0.3%
2.417383728 10
0.7%
2.307881876 14
1.0%
2.198380025 4
 
0.3%
2.088878174 14
1.0%
1.979376322 22
1.5%
1.869874471 18
1.2%
1.76037262 19
1.3%
1.650870768 18
1.2%
1.541368917 19
1.3%

DailyRate
Real number (ℝ)

Distinct886
Distinct (%)60.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.5919429 × 10-17
Minimum-1.7365757
Maximum1.7267301
Zeros0
Zeros (%)0.0%
Negative736
Negative (%)50.1%
Memory size11.6 KiB
2023-05-21T21:29:33.127375image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum-1.7365757
5-th percentile-1.5795246
Q1-0.83666159
median-0.0012041354
Q30.87887716
95-th percentile1.5410454
Maximum1.7267301
Range3.4633058
Interquartile range (IQR)1.7155387

Descriptive statistics

Standard deviation1.0003403
Coefficient of variation (CV)2.1784686 × 1016
Kurtosis-1.2038228
Mean4.5919429 × 10-17
Median Absolute Deviation (MAD)0.85281117
Skewness-0.0035185684
Sum3.08642 × 10-14
Variance1.0006807
MonotonicityNot monotonic
2023-05-21T21:29:33.243519image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-0.276384483 6
 
0.4%
-0.9779704142 5
 
0.3%
-0.6755199421 5
 
0.3%
1.30528274 5
 
0.3%
0.6929444892 5
 
0.3%
-1.17381949 5
 
0.3%
0.06573162485 4
 
0.3%
1.652357052 4
 
0.3%
-1.327523829 4
 
0.3%
-1.45147894 4
 
0.3%
Other values (876) 1423
96.8%
ValueCountFrequency (%)
-1.736575697 1
 
0.1%
-1.734096594 1
 
0.1%
-1.731617492 1
 
0.1%
-1.72913839 1
 
0.1%
-1.726659288 1
 
0.1%
-1.724180186 1
 
0.1%
-1.719221981 1
 
0.1%
-1.714263777 3
0.2%
-1.704347368 1
 
0.1%
-1.701868265 2
0.1%
ValueCountFrequency (%)
1.726730119 1
 
0.1%
1.724251017 1
 
0.1%
1.719292813 2
0.1%
1.71681371 3
0.2%
1.709376404 1
 
0.1%
1.704418199 4
0.3%
1.699459995 1
 
0.1%
1.692022688 3
0.2%
1.684585381 1
 
0.1%
1.679627177 2
0.1%

DistanceFromHome
Real number (ℝ)

Distinct29
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.5919429 × 10-17
Minimum-1.0109093
Maximum2.4441291
Zeros0
Zeros (%)0.0%
Negative940
Negative (%)63.9%
Memory size11.6 KiB
2023-05-21T21:29:33.359711image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum-1.0109093
5-th percentile-1.0109093
Q1-0.88751511
median-0.27054395
Q30.59321567
95-th percentile2.0739465
Maximum2.4441291
Range3.4550385
Interquartile range (IQR)1.4807308

Descriptive statistics

Standard deviation1.0003403
Coefficient of variation (CV)2.1784686 × 1016
Kurtosis-0.2248334
Mean4.5919429 × 10-17
Median Absolute Deviation (MAD)0.61697116
Skewness0.958118
Sum3.907985 × 10-14
Variance1.0006807
MonotonicityNot monotonic
2023-05-21T21:29:33.460479image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
-0.8875151111 211
14.4%
-1.010909343 208
14.1%
0.09963874367 86
 
5.9%
-0.02375548817 85
 
5.8%
-0.7641208792 84
 
5.7%
-0.2705439519 84
 
5.7%
-0.14714972 80
 
5.4%
-0.5173324155 65
 
4.4%
-0.6407266474 64
 
4.4%
-0.3939381837 59
 
4.0%
Other values (19) 444
30.2%
ValueCountFrequency (%)
-1.010909343 208
14.1%
-0.8875151111 211
14.4%
-0.7641208792 84
 
5.7%
-0.6407266474 64
 
4.4%
-0.5173324155 65
 
4.4%
-0.3939381837 59
 
4.0%
-0.2705439519 84
 
5.7%
-0.14714972 80
 
5.4%
-0.02375548817 85
5.8%
0.09963874367 86
5.9%
ValueCountFrequency (%)
2.444129149 27
1.8%
2.320734917 23
1.6%
2.197340685 12
0.8%
2.073946453 25
1.7%
1.950552221 25
1.7%
1.827157989 28
1.9%
1.703763758 27
1.8%
1.580369526 19
1.3%
1.456975294 18
1.2%
1.333581062 25
1.7%

Education
Categorical

Distinct5
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size11.6 KiB
0.08504925188071055
572 
1.0617867539482457
398 
-0.8916882501868245
282 
-1.8684257522543597
170 
2.038524256015781
 
48

Length

Max length19
Median length19
Mean length18.663946
Min length17

Characters and Unicode

Total characters27436
Distinct characters12
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-0.8916882501868245
2nd row-1.8684257522543597
3rd row-0.8916882501868245
4th row1.0617867539482457
5th row-1.8684257522543597

Common Values

ValueCountFrequency (%)
0.08504925188071055 572
38.9%
1.0617867539482457 398
27.1%
-0.8916882501868245 282
19.2%
-1.8684257522543597 170
 
11.6%
2.038524256015781 48
 
3.3%

Length

2023-05-21T21:29:33.576125image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-21T21:29:33.711366image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0.08504925188071055 572
38.9%
1.0617867539482457 398
27.1%
0.8916882501868245 282
19.2%
1.8684257522543597 170
 
11.6%
2.038524256015781 48
 
3.3%

Most occurring characters

ValueCountFrequency (%)
5 4472
16.3%
8 4358
15.9%
0 3918
14.3%
1 2770
10.1%
2 2188
8.0%
7 2154
7.9%
4 2038
7.4%
6 1578
 
5.8%
. 1470
 
5.4%
9 1422
 
5.2%
Other values (2) 1068
 
3.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 25514
93.0%
Other Punctuation 1470
 
5.4%
Dash Punctuation 452
 
1.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
5 4472
17.5%
8 4358
17.1%
0 3918
15.4%
1 2770
10.9%
2 2188
8.6%
7 2154
8.4%
4 2038
8.0%
6 1578
 
6.2%
9 1422
 
5.6%
3 616
 
2.4%
Other Punctuation
ValueCountFrequency (%)
. 1470
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 452
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 27436
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
5 4472
16.3%
8 4358
15.9%
0 3918
14.3%
1 2770
10.1%
2 2188
8.0%
7 2154
7.9%
4 2038
7.4%
6 1578
 
5.8%
. 1470
 
5.4%
9 1422
 
5.2%
Other values (2) 1068
 
3.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 27436
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
5 4472
16.3%
8 4358
15.9%
0 3918
14.3%
1 2770
10.1%
2 2188
8.0%
7 2154
7.9%
4 2038
7.4%
6 1578
 
5.8%
. 1470
 
5.4%
9 1422
 
5.2%
Other values (2) 1068
 
3.9%
Distinct4
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size11.6 KiB
0.2546249253678617
453 
1.1697805251007627
446 
-0.6605306743650393
287 
-1.5756862740979403
284 

Length

Max length19
Median length18
Mean length18.388435
Min length18

Characters and Unicode

Total characters27031
Distinct characters12
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-0.6605306743650393
2nd row0.2546249253678617
3rd row1.1697805251007627
4th row1.1697805251007627
5th row-1.5756862740979403

Common Values

ValueCountFrequency (%)
0.2546249253678617 453
30.8%
1.1697805251007627 446
30.3%
-0.6605306743650393 287
19.5%
-1.5756862740979403 284
19.3%

Length

2023-05-21T21:29:33.822030image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-21T21:29:33.931059image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0.2546249253678617 453
30.8%
1.1697805251007627 446
30.3%
0.6605306743650393 287
19.5%
1.5756862740979403 284
19.3%

Most occurring characters

ValueCountFrequency (%)
6 3967
14.7%
0 3507
13.0%
7 3383
12.5%
5 2940
10.9%
2 2535
9.4%
1 2075
7.7%
3 1885
7.0%
4 1761
6.5%
9 1754
6.5%
. 1470
 
5.4%
Other values (2) 1754
6.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 24990
92.4%
Other Punctuation 1470
 
5.4%
Dash Punctuation 571
 
2.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
6 3967
15.9%
0 3507
14.0%
7 3383
13.5%
5 2940
11.8%
2 2535
10.1%
1 2075
8.3%
3 1885
7.5%
4 1761
7.0%
9 1754
7.0%
8 1183
 
4.7%
Other Punctuation
ValueCountFrequency (%)
. 1470
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 571
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 27031
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
6 3967
14.7%
0 3507
13.0%
7 3383
12.5%
5 2940
10.9%
2 2535
9.4%
1 2075
7.7%
3 1885
7.0%
4 1761
6.5%
9 1754
6.5%
. 1470
 
5.4%
Other values (2) 1754
6.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 27031
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
6 3967
14.7%
0 3507
13.0%
7 3383
12.5%
5 2940
10.9%
2 2535
9.4%
1 2075
7.7%
3 1885
7.0%
4 1761
6.5%
9 1754
6.5%
. 1470
 
5.4%
Other values (2) 1754
6.5%

JobInvolvement
Categorical

Distinct4
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size11.6 KiB
0.3796721288811475
868 
-1.0261667362455194
375 
1.7855109940078144
144 
-2.432005601372186
 
83

Length

Max length19
Median length18
Mean length18.255102
Min length18

Characters and Unicode

Total characters26835
Distinct characters12
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.3796721288811475
2nd row-1.0261667362455194
3rd row-1.0261667362455194
4th row0.3796721288811475
5th row0.3796721288811475

Common Values

ValueCountFrequency (%)
0.3796721288811475 868
59.0%
-1.0261667362455194 375
25.5%
1.7855109940078144 144
 
9.8%
-2.432005601372186 83
 
5.6%

Length

2023-05-21T21:29:34.036281image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-21T21:29:34.146988image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0.3796721288811475 868
59.0%
1.0261667362455194 375
25.5%
1.7855109940078144 144
 
9.8%
2.432005601372186 83
 
5.6%

Most occurring characters

ValueCountFrequency (%)
1 4327
16.1%
7 3350
12.5%
8 2975
11.1%
2 2735
10.2%
6 2534
9.4%
4 2133
7.9%
5 1989
7.4%
0 1924
7.2%
9 1531
 
5.7%
. 1470
 
5.5%
Other values (2) 1867
7.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 24907
92.8%
Other Punctuation 1470
 
5.5%
Dash Punctuation 458
 
1.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 4327
17.4%
7 3350
13.5%
8 2975
11.9%
2 2735
11.0%
6 2534
10.2%
4 2133
8.6%
5 1989
8.0%
0 1924
7.7%
9 1531
 
6.1%
3 1409
 
5.7%
Other Punctuation
ValueCountFrequency (%)
. 1470
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 458
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 26835
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 4327
16.1%
7 3350
12.5%
8 2975
11.1%
2 2735
10.2%
6 2534
9.4%
4 2133
7.9%
5 1989
7.4%
0 1924
7.2%
9 1531
 
5.7%
. 1470
 
5.5%
Other values (2) 1867
7.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 26835
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 4327
16.1%
7 3350
12.5%
8 2975
11.1%
2 2735
10.2%
6 2534
9.4%
4 2133
7.9%
5 1989
7.4%
0 1924
7.2%
9 1531
 
5.7%
. 1470
 
5.5%
Other values (2) 1867
7.0%

JobLevel
Categorical

Distinct5
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size11.6 KiB
-0.9614863916702531
543 
-0.05778754527941421
534 
0.8459113011114247
218 
1.7496101475022634
106 
2.6533089938931025
69 

Length

Max length20
Median length19
Mean length19.095918
Min length18

Characters and Unicode

Total characters28071
Distinct characters12
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-0.05778754527941421
2nd row-0.05778754527941421
3rd row-0.9614863916702531
4th row-0.9614863916702531
5th row-0.9614863916702531

Common Values

ValueCountFrequency (%)
-0.9614863916702531 543
36.9%
-0.05778754527941421 534
36.3%
0.8459113011114247 218
14.8%
1.7496101475022634 106
 
7.2%
2.6533089938931025 69
 
4.7%

Length

2023-05-21T21:29:34.254387image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-21T21:29:34.377054image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0.9614863916702531 543
36.9%
0.05778754527941421 534
36.3%
0.8459113011114247 218
14.8%
1.7496101475022634 106
 
7.2%
2.6533089938931025 69
 
4.7%

Most occurring characters

ValueCountFrequency (%)
1 4392
15.6%
4 3117
11.1%
7 3109
11.1%
0 2940
10.5%
5 2607
9.3%
2 2179
7.8%
9 2151
7.7%
6 1910
6.8%
3 1686
 
6.0%
. 1470
 
5.2%
Other values (2) 2510
8.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 25524
90.9%
Other Punctuation 1470
 
5.2%
Dash Punctuation 1077
 
3.8%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 4392
17.2%
4 3117
12.2%
7 3109
12.2%
0 2940
11.5%
5 2607
10.2%
2 2179
8.5%
9 2151
8.4%
6 1910
7.5%
3 1686
 
6.6%
8 1433
 
5.6%
Other Punctuation
ValueCountFrequency (%)
. 1470
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1077
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 28071
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 4392
15.6%
4 3117
11.1%
7 3109
11.1%
0 2940
10.5%
5 2607
9.3%
2 2179
7.8%
9 2151
7.7%
6 1910
6.8%
3 1686
 
6.0%
. 1470
 
5.2%
Other values (2) 2510
8.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 28071
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 4392
15.6%
4 3117
11.1%
7 3109
11.1%
0 2940
10.5%
5 2607
9.3%
2 2179
7.8%
9 2151
7.7%
6 1910
6.8%
3 1686
 
6.0%
. 1470
 
5.2%
Other values (2) 2510
8.9%

JobSatisfaction
Categorical

Distinct4
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size11.6 KiB
1.1532535902386967
459 
0.24620020465769926
442 
-1.5679065665042957
289 
-0.6608531809232983
280 

Length

Max length19
Median length19
Mean length18.687755
Min length18

Characters and Unicode

Total characters27471
Distinct characters12
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.1532535902386967
2nd row-0.6608531809232983
3rd row0.24620020465769926
4th row0.24620020465769926
5th row-0.6608531809232983

Common Values

ValueCountFrequency (%)
1.1532535902386967 459
31.2%
0.24620020465769926 442
30.1%
-1.5679065665042957 289
19.7%
-0.6608531809232983 280
19.0%

Length

2023-05-21T21:29:34.484490image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-21T21:29:34.597676image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
1.1532535902386967 459
31.2%
0.24620020465769926 442
30.1%
1.5679065665042957 289
19.7%
0.6608531809232983 280
19.0%

Most occurring characters

ValueCountFrequency (%)
6 4402
16.0%
0 3645
13.3%
2 3535
12.9%
5 3255
11.8%
9 2940
10.7%
3 2217
8.1%
1 1487
 
5.4%
7 1479
 
5.4%
. 1470
 
5.4%
8 1299
 
4.7%
Other values (2) 1742
 
6.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 25432
92.6%
Other Punctuation 1470
 
5.4%
Dash Punctuation 569
 
2.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
6 4402
17.3%
0 3645
14.3%
2 3535
13.9%
5 3255
12.8%
9 2940
11.6%
3 2217
8.7%
1 1487
 
5.8%
7 1479
 
5.8%
8 1299
 
5.1%
4 1173
 
4.6%
Other Punctuation
ValueCountFrequency (%)
. 1470
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 569
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 27471
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
6 4402
16.0%
0 3645
13.3%
2 3535
12.9%
5 3255
11.8%
9 2940
10.7%
3 2217
8.1%
1 1487
 
5.4%
7 1479
 
5.4%
. 1470
 
5.4%
8 1299
 
4.7%
Other values (2) 1742
 
6.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 27471
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
6 4402
16.0%
0 3645
13.3%
2 3535
12.9%
5 3255
11.8%
9 2940
10.7%
3 2217
8.1%
1 1487
 
5.4%
7 1479
 
5.4%
. 1470
 
5.4%
8 1299
 
4.7%
Other values (2) 1742
 
6.3%

MonthlyIncome
Real number (ℝ)

Distinct1349
Distinct (%)91.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-4.3502616 × 10-17
Minimum-1.1673431
Maximum2.8676265
Zeros0
Zeros (%)0.0%
Negative977
Negative (%)66.5%
Memory size11.6 KiB
2023-05-21T21:29:34.721127image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum-1.1673431
5-th percentile-0.93597511
Q1-0.76320872
median-0.33655159
Q30.39862455
95-th percentile2.4049223
Maximum2.8676265
Range4.0349696
Interquartile range (IQR)1.1618333

Descriptive statistics

Standard deviation1.0003403
Coefficient of variation (CV)-2.2994946 × 1016
Kurtosis1.0052327
Mean-4.3502616 × 10-17
Median Absolute Deviation (MAD)0.46724056
Skewness1.3698167
Sum-6.4170891 × 10-14
Variance1.0006807
MonotonicityNot monotonic
2023-05-21T21:29:34.853737image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-0.884109071 4
 
0.3%
-0.0766901944 3
 
0.2%
-0.7993300889 3
 
0.2%
-0.8380012036 3
 
0.2%
-0.8271647923 3
 
0.2%
-0.860948898 3
 
0.2%
-0.1999278124 3
 
0.2%
-0.6482577676 3
 
0.2%
-0.8760348822 3
 
0.2%
-0.0331320708 3
 
0.2%
Other values (1339) 1439
97.9%
ValueCountFrequency (%)
-1.167343114 1
0.1%
-1.15841901 1
0.1%
-1.158206532 1
0.1%
-1.152044651 1
0.1%
-1.149919864 1
0.1%
-1.147582599 1
0.1%
-1.144182941 1
0.1%
-1.141845676 1
0.1%
-1.126759691 1
0.1%
-1.121872682 1
0.1%
ValueCountFrequency (%)
2.867626483 1
0.1%
2.862102038 1
0.1%
2.855727678 1
0.1%
2.852115541 1
0.1%
2.837879471 1
0.1%
2.835329728 1
0.1%
2.83490477 1
0.1%
2.832355026 1
0.1%
2.812594512 1
0.1%
2.807707503 1
0.1%

NumCompaniesWorked
Real number (ℝ)

Distinct10
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.8668992 × 10-17
Minimum-1.0785044
Maximum2.525591
Zeros0
Zeros (%)0.0%
Negative864
Negative (%)58.8%
Memory size11.6 KiB
2023-05-21T21:29:34.966311image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum-1.0785044
5-th percentile-1.0785044
Q1-0.67804939
median-0.27759435
Q30.52331574
95-th percentile2.1251359
Maximum2.525591
Range3.6040954
Interquartile range (IQR)1.2013651

Descriptive statistics

Standard deviation1.0003403
Coefficient of variation (CV)2.5869314 × 1016
Kurtosis0.010213817
Mean3.8668992 × 10-17
Median Absolute Deviation (MAD)0.40045505
Skewness1.0264711
Sum5.6843419 × 10-14
Variance1.0006807
MonotonicityNot monotonic
2023-05-21T21:29:35.051358image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
-0.678049393 521
35.4%
-1.078504438 197
 
13.4%
0.1228606976 159
 
10.8%
-0.2775943477 146
 
9.9%
0.5233157429 139
 
9.5%
1.724680879 74
 
5.0%
1.324225833 70
 
4.8%
0.9237707882 63
 
4.3%
2.525590969 52
 
3.5%
2.125135924 49
 
3.3%
ValueCountFrequency (%)
-1.078504438 197
 
13.4%
-0.678049393 521
35.4%
-0.2775943477 146
 
9.9%
0.1228606976 159
 
10.8%
0.5233157429 139
 
9.5%
0.9237707882 63
 
4.3%
1.324225833 70
 
4.8%
1.724680879 74
 
5.0%
2.125135924 49
 
3.3%
2.525590969 52
 
3.5%
ValueCountFrequency (%)
2.525590969 52
 
3.5%
2.125135924 49
 
3.3%
1.724680879 74
 
5.0%
1.324225833 70
 
4.8%
0.9237707882 63
 
4.3%
0.5233157429 139
 
9.5%
0.1228606976 159
 
10.8%
-0.2775943477 146
 
9.9%
-0.678049393 521
35.4%
-1.078504438 197
 
13.4%

PercentSalaryHike
Real number (ℝ)

Distinct15
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.9485107 × 10-16
Minimum-1.1505541
Maximum2.6759494
Zeros0
Zeros (%)0.0%
Negative919
Negative (%)62.5%
Memory size11.6 KiB
2023-05-21T21:29:35.149004image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum-1.1505541
5-th percentile-1.1505541
Q1-0.87723243
median-0.33058907
Q30.76269763
95-th percentile1.8559843
Maximum2.6759494
Range3.8265035
Interquartile range (IQR)1.6399301

Descriptive statistics

Standard deviation1.0003403
Coefficient of variation (CV)3.3926969 × 1015
Kurtosis-0.30059822
Mean2.9485107 × 10-16
Median Absolute Deviation (MAD)0.54664335
Skewness0.82112798
Sum4.3343107 × 10-13
Variance1.0006807
MonotonicityNot monotonic
2023-05-21T21:29:35.235685image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
-1.150554102 210
14.3%
-0.6039107501 209
14.2%
-0.3305890744 201
13.7%
-0.8772324259 198
13.5%
-0.05726739872 101
6.9%
0.7626976284 89
6.1%
0.4893759527 82
 
5.6%
0.216054277 78
 
5.3%
1.036019304 76
 
5.2%
1.855984331 56
 
3.8%
Other values (5) 170
11.6%
ValueCountFrequency (%)
-1.150554102 210
14.3%
-0.8772324259 198
13.5%
-0.6039107501 209
14.2%
-0.3305890744 201
13.7%
-0.05726739872 101
6.9%
0.216054277 78
 
5.3%
0.4893759527 82
 
5.6%
0.7626976284 89
6.1%
1.036019304 76
 
5.2%
1.30934098 55
 
3.7%
ValueCountFrequency (%)
2.675949358 18
 
1.2%
2.402627683 21
 
1.4%
2.129306007 28
 
1.9%
1.855984331 56
3.8%
1.582662656 48
3.3%
1.30934098 55
3.7%
1.036019304 76
5.2%
0.7626976284 89
6.1%
0.4893759527 82
5.6%
0.216054277 78
5.3%
Distinct4
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size11.6 KiB
0.26623257679518353
459 
1.1914379855160337
432 
-0.6589728319256667
303 
-1.584178240646517
276 

Length

Max length19
Median length19
Mean length18.518367
Min length18

Characters and Unicode

Total characters27222
Distinct characters12
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-1.584178240646517
2nd row1.1914379855160337
3rd row-0.6589728319256667
4th row0.26623257679518353
5th row1.1914379855160337

Common Values

ValueCountFrequency (%)
0.26623257679518353 459
31.2%
1.1914379855160337 432
29.4%
-0.6589728319256667 303
20.6%
-1.584178240646517 276
18.8%

Length

2023-05-21T21:29:35.336662image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-21T21:29:35.446460image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0.26623257679518353 459
31.2%
1.1914379855160337 432
29.4%
0.6589728319256667 303
20.6%
1.584178240646517 276
18.8%

Most occurring characters

ValueCountFrequency (%)
6 3573
13.1%
5 3399
12.5%
1 3318
12.2%
3 2976
10.9%
7 2940
10.8%
2 2259
8.3%
8 2049
7.5%
9 1929
7.1%
0 1470
5.4%
. 1470
5.4%
Other values (2) 1839
6.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 25173
92.5%
Other Punctuation 1470
 
5.4%
Dash Punctuation 579
 
2.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
6 3573
14.2%
5 3399
13.5%
1 3318
13.2%
3 2976
11.8%
7 2940
11.7%
2 2259
9.0%
8 2049
8.1%
9 1929
7.7%
0 1470
5.8%
4 1260
 
5.0%
Other Punctuation
ValueCountFrequency (%)
. 1470
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 579
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 27222
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
6 3573
13.1%
5 3399
12.5%
1 3318
12.2%
3 2976
10.9%
7 2940
10.8%
2 2259
8.3%
8 2049
7.5%
9 1929
7.1%
0 1470
5.4%
. 1470
5.4%
Other values (2) 1839
6.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 27222
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
6 3573
13.1%
5 3399
12.5%
1 3318
12.2%
3 2976
10.9%
7 2940
10.8%
2 2259
8.3%
8 2049
7.5%
9 1929
7.1%
0 1470
5.4%
. 1470
5.4%
Other values (2) 1839
6.8%

StockOptionLevel
Categorical

Distinct4
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size11.6 KiB
-0.9320143892374165
631 
0.24198831185855807
596 
1.4159910129545328
158 
2.5899937140505074
85 

Length

Max length19
Median length19
Mean length18.834694
Min length18

Characters and Unicode

Total characters27687
Distinct characters12
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-0.9320143892374165
2nd row0.24198831185855807
3rd row-0.9320143892374165
4th row-0.9320143892374165
5th row0.24198831185855807

Common Values

ValueCountFrequency (%)
-0.9320143892374165 631
42.9%
0.24198831185855807 596
40.5%
1.4159910129545328 158
 
10.7%
2.5899937140505074 85
 
5.8%

Length

2023-05-21T21:29:35.552393image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-21T21:29:35.666752image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0.9320143892374165 631
42.9%
0.24198831185855807 596
40.5%
1.4159910129545328 158
 
10.7%
2.5899937140505074 85
 
5.8%

Most occurring characters

ValueCountFrequency (%)
8 3854
13.9%
1 3767
13.6%
5 3148
11.4%
0 2867
10.4%
3 2732
9.9%
9 2587
9.3%
4 2344
8.5%
2 2259
8.2%
. 1470
 
5.3%
7 1397
 
5.0%
Other values (2) 1262
 
4.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 25586
92.4%
Other Punctuation 1470
 
5.3%
Dash Punctuation 631
 
2.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
8 3854
15.1%
1 3767
14.7%
5 3148
12.3%
0 2867
11.2%
3 2732
10.7%
9 2587
10.1%
4 2344
9.2%
2 2259
8.8%
7 1397
 
5.5%
6 631
 
2.5%
Other Punctuation
ValueCountFrequency (%)
. 1470
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 631
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 27687
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
8 3854
13.9%
1 3767
13.6%
5 3148
11.4%
0 2867
10.4%
3 2732
9.9%
9 2587
9.3%
4 2344
8.5%
2 2259
8.2%
. 1470
 
5.3%
7 1397
 
5.0%
Other values (2) 1262
 
4.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 27687
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
8 3854
13.9%
1 3767
13.6%
5 3148
11.4%
0 2867
10.4%
3 2732
9.9%
9 2587
9.3%
4 2344
8.5%
2 2259
8.2%
. 1470
 
5.3%
7 1397
 
5.0%
Other values (2) 1262
 
4.6%

TotalWorkingYears
Real number (ℝ)

Distinct40
Distinct (%)2.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-1.9334496 × 10-17
Minimum-1.4501667
Maximum3.6924545
Zeros0
Zeros (%)0.0%
Negative959
Negative (%)65.2%
Memory size11.6 KiB
2023-05-21T21:29:35.868349image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum-1.4501667
5-th percentile-1.3216012
Q1-0.67877352
median-0.1645114
Q30.47831624
95-th percentile2.1496681
Maximum3.6924545
Range5.1426211
Interquartile range (IQR)1.1570898

Descriptive statistics

Standard deviation1.0003403
Coefficient of variation (CV)-5.1738628 × 1016
Kurtosis0.91826954
Mean-1.9334496 × 10-17
Median Absolute Deviation (MAD)0.51426211
Skewness1.1171719
Sum-1.4210855 × 10-14
Variance1.0006807
MonotonicityNot monotonic
2023-05-21T21:29:35.976734image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=40)
ValueCountFrequency (%)
-0.164511401 202
 
13.7%
-0.6787735157 125
 
8.5%
-0.4216424584 103
 
7.0%
-0.2930769297 96
 
6.5%
-0.8073390444 88
 
6.0%
-0.550207987 81
 
5.5%
-1.321601159 81
 
5.5%
-0.9359045731 63
 
4.3%
0.09261965638 48
 
3.3%
-1.064470102 42
 
2.9%
Other values (30) 541
36.8%
ValueCountFrequency (%)
-1.450166688 11
 
0.7%
-1.321601159 81
5.5%
-1.19303563 31
 
2.1%
-1.064470102 42
 
2.9%
-0.9359045731 63
4.3%
-0.8073390444 88
6.0%
-0.6787735157 125
8.5%
-0.550207987 81
5.5%
-0.4216424584 103
7.0%
-0.2930769297 96
6.5%
ValueCountFrequency (%)
3.69245446 2
 
0.1%
3.435323402 1
 
0.1%
3.306757873 4
0.3%
3.178192345 6
0.4%
3.049626816 3
 
0.2%
2.921061287 5
0.3%
2.792495759 7
0.5%
2.66393023 9
0.6%
2.535364701 9
0.6%
2.406799173 7
0.5%

TrainingTimesLastYear
Real number (ℝ)

Distinct7
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.7005233 × 10-17
Minimum-2.1719818
Maximum2.483396
Zeros0
Zeros (%)0.0%
Negative672
Negative (%)45.7%
Memory size11.6 KiB
2023-05-21T21:29:36.064769image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum-2.1719818
5-th percentile-1.3960855
Q1-0.62018922
median0.15570708
Q30.15570708
95-th percentile1.7074997
Maximum2.483396
Range4.6553778
Interquartile range (IQR)0.7758963

Descriptive statistics

Standard deviation1.0003403
Coefficient of variation (CV)1.1497473 × 1016
Kurtosis0.49499299
Mean8.7005233 × 10-17
Median Absolute Deviation (MAD)0.7758963
Skewness0.55312417
Sum1.2789769 × 10-13
Variance1.0006807
MonotonicityNot monotonic
2023-05-21T21:29:36.142399image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
-0.6201892227 547
37.2%
0.1557070814 491
33.4%
0.9316033856 123
 
8.4%
1.70749969 119
 
8.1%
-1.396085527 71
 
4.8%
2.483395994 65
 
4.4%
-2.171981831 54
 
3.7%
ValueCountFrequency (%)
-2.171981831 54
 
3.7%
-1.396085527 71
 
4.8%
-0.6201892227 547
37.2%
0.1557070814 491
33.4%
0.9316033856 123
 
8.4%
1.70749969 119
 
8.1%
2.483395994 65
 
4.4%
ValueCountFrequency (%)
2.483395994 65
 
4.4%
1.70749969 119
 
8.1%
0.9316033856 123
 
8.4%
0.1557070814 491
33.4%
-0.6201892227 547
37.2%
-1.396085527 71
 
4.8%
-2.171981831 54
 
3.7%

WorkLifeBalance
Categorical

Distinct4
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size11.6 KiB
0.33809616377248186
893 
-1.0778621289498782
344 
1.754054456494842
153 
-2.4938204216722384
 
80

Length

Max length19
Median length19
Mean length18.791837
Min length17

Characters and Unicode

Total characters27624
Distinct characters12
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-2.4938204216722384
2nd row0.33809616377248186
3rd row0.33809616377248186
4th row0.33809616377248186
5th row0.33809616377248186

Common Values

ValueCountFrequency (%)
0.33809616377248186 893
60.7%
-1.0778621289498782 344
 
23.4%
1.754054456494842 153
 
10.4%
-2.4938204216722384 80
 
5.4%

Length

2023-05-21T21:29:36.237203image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-21T21:29:36.361355image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0.33809616377248186 893
60.7%
1.0778621289498782 344
 
23.4%
1.754054456494842 153
 
10.4%
2.4938204216722384 80
 
5.4%

Most occurring characters

ValueCountFrequency (%)
8 4368
15.8%
6 3256
11.8%
7 3051
11.0%
3 2839
10.3%
1 2707
9.8%
2 2478
9.0%
4 2395
8.7%
0 2363
8.6%
9 1814
6.6%
. 1470
 
5.3%
Other values (2) 883
 
3.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 25730
93.1%
Other Punctuation 1470
 
5.3%
Dash Punctuation 424
 
1.5%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
8 4368
17.0%
6 3256
12.7%
7 3051
11.9%
3 2839
11.0%
1 2707
10.5%
2 2478
9.6%
4 2395
9.3%
0 2363
9.2%
9 1814
7.1%
5 459
 
1.8%
Other Punctuation
ValueCountFrequency (%)
. 1470
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 424
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 27624
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
8 4368
15.8%
6 3256
11.8%
7 3051
11.0%
3 2839
10.3%
1 2707
9.8%
2 2478
9.0%
4 2395
8.7%
0 2363
8.6%
9 1814
6.6%
. 1470
 
5.3%
Other values (2) 883
 
3.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 27624
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
8 4368
15.8%
6 3256
11.8%
7 3051
11.0%
3 2839
10.3%
1 2707
9.8%
2 2478
9.0%
4 2395
8.7%
0 2363
8.6%
9 1814
6.6%
. 1470
 
5.3%
Other values (2) 883
 
3.2%

YearsAtCompany
Real number (ℝ)

Distinct37
Distinct (%)2.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-8.2775812 × 10-17
Minimum-1.1442944
Maximum5.386914
Zeros0
Zeros (%)0.0%
Negative942
Negative (%)64.1%
Memory size11.6 KiB
2023-05-21T21:29:36.470913image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum-1.1442944
5-th percentile-0.98101416
Q1-0.65445374
median-0.32789332
Q30.32522752
95-th percentile2.1213098
Maximum5.386914
Range6.5312084
Interquartile range (IQR)0.97968126

Descriptive statistics

Standard deviation1.0003403
Coefficient of variation (CV)-1.2084935 × 1016
Kurtosis3.9355088
Mean-8.2775812 × 10-17
Median Absolute Deviation (MAD)0.48984063
Skewness1.7645295
Sum-9.3258734 × 10-14
Variance1.0006807
MonotonicityNot monotonic
2023-05-21T21:29:36.575423image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=37)
ValueCountFrequency (%)
-0.3278933184 196
13.3%
-0.981014156 171
11.6%
-0.6544537372 128
8.7%
-0.8177339466 127
8.6%
0.4885077285 120
8.2%
-0.4911735278 110
 
7.5%
-0.001332899668 90
 
6.1%
0.3252275191 82
 
5.6%
0.1619473097 80
 
5.4%
-0.1646131091 76
 
5.2%
Other values (27) 290
19.7%
ValueCountFrequency (%)
-1.144294365 44
 
3.0%
-0.981014156 171
11.6%
-0.8177339466 127
8.6%
-0.6544537372 128
8.7%
-0.4911735278 110
7.5%
-0.3278933184 196
13.3%
-0.1646131091 76
 
5.2%
-0.001332899668 90
6.1%
0.1619473097 80
5.4%
0.3252275191 82
5.6%
ValueCountFrequency (%)
5.38691401 1
 
0.1%
4.897073382 1
 
0.1%
4.733793173 2
 
0.1%
4.407232754 1
 
0.1%
4.243952544 5
0.3%
4.080672335 3
0.2%
3.917392126 3
0.2%
3.754111916 1
 
0.1%
3.590831707 2
 
0.1%
3.264271288 2
 
0.1%

YearsInCurrentRole
Real number (ℝ)

Distinct19
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.1234654 × 10-17
Minimum-1.1676873
Maximum3.8020739
Zeros0
Zeros (%)0.0%
Negative912
Negative (%)62.0%
Memory size11.6 KiB
2023-05-21T21:29:36.681456image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum-1.1676873
5-th percentile-1.1676873
Q1-0.61549158
median-0.33939374
Q30.76499762
95-th percentile1.869389
Maximum3.8020739
Range4.9697611
Interquartile range (IQR)1.3804892

Descriptive statistics

Standard deviation1.0003403
Coefficient of variation (CV)1.0964477 × 1016
Kurtosis0.47742077
Mean9.1234654 × 10-17
Median Absolute Deviation (MAD)0.82829352
Skewness0.91736316
Sum1.3411494 × 10-13
Variance1.0006807
MonotonicityNot monotonic
2023-05-21T21:29:36.788172image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
-0.6154915796 372
25.3%
-1.16768726 244
16.6%
0.7649976209 222
15.1%
-0.3393937395 135
 
9.2%
-0.0632958994 104
 
7.1%
1.041095461 89
 
6.1%
1.317193301 67
 
4.6%
-0.8915894197 57
 
3.9%
0.4888997808 37
 
2.5%
0.2128019407 36
 
2.4%
Other values (9) 107
 
7.3%
ValueCountFrequency (%)
-1.16768726 244
16.6%
-0.8915894197 57
 
3.9%
-0.6154915796 372
25.3%
-0.3393937395 135
 
9.2%
-0.0632958994 104
 
7.1%
0.2128019407 36
 
2.4%
0.4888997808 37
 
2.5%
0.7649976209 222
15.1%
1.041095461 89
 
6.1%
1.317193301 67
 
4.6%
ValueCountFrequency (%)
3.802073862 2
 
0.1%
3.525976022 4
 
0.3%
3.249878182 7
 
0.5%
2.973780342 8
 
0.5%
2.697682502 11
 
0.7%
2.421584662 14
 
1.0%
2.145486821 10
 
0.7%
1.869388981 22
 
1.5%
1.593291141 29
2.0%
1.317193301 67
4.6%

YearsSinceLastPromotion
Real number (ℝ)

Distinct16
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0
Minimum-0.67914568
Maximum3.9773103
Zeros0
Zeros (%)0.0%
Negative1097
Negative (%)74.6%
Memory size11.6 KiB
2023-05-21T21:29:36.884649image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum-0.67914568
5-th percentile-0.67914568
Q1-0.67914568
median-0.36871529
Q30.25214551
95-th percentile2.1147279
Maximum3.9773103
Range4.6564559
Interquartile range (IQR)0.93129119

Descriptive statistics

Standard deviation1.0003403
Coefficient of variation (CV)nan
Kurtosis3.6126731
Mean0
Median Absolute Deviation (MAD)0.3104304
Skewness1.98429
Sum-5.6843419 × 10-14
Variance1.0006807
MonotonicityNot monotonic
2023-05-21T21:29:36.974628image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
-0.6791456841 581
39.5%
-0.3687152874 357
24.3%
-0.0582848908 159
 
10.8%
1.493867092 76
 
5.2%
0.5625759025 61
 
4.1%
0.2521455058 52
 
3.5%
0.8730062991 45
 
3.1%
1.183436696 32
 
2.2%
2.735588679 24
 
1.6%
1.804297489 18
 
1.2%
Other values (6) 65
 
4.4%
ValueCountFrequency (%)
-0.6791456841 581
39.5%
-0.3687152874 357
24.3%
-0.0582848908 159
 
10.8%
0.2521455058 52
 
3.5%
0.5625759025 61
 
4.1%
0.8730062991 45
 
3.1%
1.183436696 32
 
2.2%
1.493867092 76
 
5.2%
1.804297489 18
 
1.2%
2.114727886 17
 
1.2%
ValueCountFrequency (%)
3.977310265 13
 
0.9%
3.666879869 9
 
0.6%
3.356449472 10
 
0.7%
3.046019076 10
 
0.7%
2.735588679 24
 
1.6%
2.425158282 6
 
0.4%
2.114727886 17
 
1.2%
1.804297489 18
 
1.2%
1.493867092 76
5.2%
1.183436696 32
2.2%

YearsWithCurrManager
Real number (ℝ)

Distinct18
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-5.2565662 × 10-17
Minimum-1.1559347
Maximum3.6100789
Zeros0
Zeros (%)0.0%
Negative923
Negative (%)62.8%
Memory size11.6 KiB
2023-05-21T21:29:37.075773image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum-1.1559347
5-th percentile-1.1559347
Q1-0.59522723
median-0.31487349
Q30.80654148
95-th percentile1.6476027
Maximum3.6100789
Range4.7660136
Interquartile range (IQR)1.4017687

Descriptive statistics

Standard deviation1.0003403
Coefficient of variation (CV)-1.90303 × 1016
Kurtosis0.17105808
Mean-5.2565662 × 10-17
Median Absolute Deviation (MAD)0.84106122
Skewness0.83345099
Sum-1.0569323 × 10-13
Variance1.0006807
MonotonicityNot monotonic
2023-05-21T21:29:37.261277image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
-0.5952272283 344
23.4%
-1.15593471 263
17.9%
0.8065414766 216
14.7%
-0.3148734873 142
9.7%
1.086895218 107
 
7.3%
-0.03451974634 98
 
6.7%
-0.8755809693 76
 
5.2%
1.367248959 64
 
4.4%
0.2458339946 31
 
2.1%
0.5261877356 29
 
2.0%
Other values (8) 100
 
6.8%
ValueCountFrequency (%)
-1.15593471 263
17.9%
-0.8755809693 76
 
5.2%
-0.5952272283 344
23.4%
-0.3148734873 142
9.7%
-0.03451974634 98
 
6.7%
0.2458339946 31
 
2.1%
0.5261877356 29
 
2.0%
0.8065414766 216
14.7%
1.086895218 107
 
7.3%
1.367248959 64
 
4.4%
ValueCountFrequency (%)
3.610078886 7
 
0.5%
3.329725145 2
 
0.1%
3.049371404 5
 
0.3%
2.769017663 5
 
0.3%
2.488663922 14
 
1.0%
2.208310181 18
 
1.2%
1.92795644 22
 
1.5%
1.647602699 27
 
1.8%
1.367248959 64
4.4%
1.086895218 107
7.3%

AverageSatisfaction
Real number (ℝ)

Distinct10
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-3.8668992 × 10-16
Minimum-2.7401096
Maximum2.0367582
Zeros0
Zeros (%)0.0%
Negative823
Negative (%)56.0%
Memory size11.6 KiB
2023-05-21T21:29:37.356050image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum-2.7401096
5-th percentile-1.6785834
Q1-0.61705722
median-0.086294135
Q30.97523204
95-th percentile1.5059951
Maximum2.0367582
Range4.7768678
Interquartile range (IQR)1.5922893

Descriptive statistics

Standard deviation1.0003403
Coefficient of variation (CV)-2.5869314 × 1015
Kurtosis-0.35796398
Mean-3.8668992 × 10-16
Median Absolute Deviation (MAD)0.53076309
Skewness-0.17619688
Sum-5.6843419 × 10-13
Variance1.0006807
MonotonicityNot monotonic
2023-05-21T21:29:37.437073image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
-0.08629413496 290
19.7%
0.4444689545 269
18.3%
-0.6170572244 244
16.6%
0.975232044 214
14.6%
-1.147820314 173
11.8%
1.505995133 124
8.4%
-1.678583403 80
 
5.4%
2.036758223 40
 
2.7%
-2.209346493 20
 
1.4%
-2.740109582 16
 
1.1%
ValueCountFrequency (%)
-2.740109582 16
 
1.1%
-2.209346493 20
 
1.4%
-1.678583403 80
 
5.4%
-1.147820314 173
11.8%
-0.6170572244 244
16.6%
-0.08629413496 290
19.7%
0.4444689545 269
18.3%
0.975232044 214
14.6%
1.505995133 124
8.4%
2.036758223 40
 
2.7%
ValueCountFrequency (%)
2.036758223 40
 
2.7%
1.505995133 124
8.4%
0.975232044 214
14.6%
0.4444689545 269
18.3%
-0.08629413496 290
19.7%
-0.6170572244 244
16.6%
-1.147820314 173
11.8%
-1.678583403 80
 
5.4%
-2.209346493 20
 
1.4%
-2.740109582 16
 
1.1%

SalaryDeviation
Real number (ℝ)

Distinct1362
Distinct (%)92.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.2504361 × 10-18
Minimum-3.5793987
Maximum3.4927509
Zeros0
Zeros (%)0.0%
Negative805
Negative (%)54.8%
Memory size11.6 KiB
2023-05-21T21:29:37.551898image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum-3.5793987
5-th percentile-1.6159366
Q1-0.55407852
median-0.072963283
Q30.53923595
95-th percentile1.6686327
Maximum3.4927509
Range7.0721496
Interquartile range (IQR)1.0933145

Descriptive statistics

Standard deviation1.0003403
Coefficient of variation (CV)1.3796968 × 1017
Kurtosis1.5717574
Mean7.2504361 × 10-18
Median Absolute Deviation (MAD)0.54384337
Skewness0.444398
Sum3.3639758 × 10-14
Variance1.0006807
MonotonicityNot monotonic
2023-05-21T21:29:37.670979image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-0.3456570438 4
 
0.3%
0.6562696573 3
 
0.2%
-0.260974366 3
 
0.2%
0.4970040706 3
 
0.2%
0.04639899599 3
 
0.2%
-0.1770685935 3
 
0.2%
-0.3161346423 3
 
0.2%
0.5167078404 3
 
0.2%
-0.2974889151 3
 
0.2%
-0.1374464231 3
 
0.2%
Other values (1352) 1439
97.9%
ValueCountFrequency (%)
-3.57939873 1
0.1%
-3.446547924 1
0.1%
-3.418991656 1
0.1%
-3.308671103 1
0.1%
-2.84952007 1
0.1%
-2.698023537 1
0.1%
-2.688307664 1
0.1%
-2.65412383 1
0.1%
-2.617609281 1
0.1%
-2.590810605 1
0.1%
ValueCountFrequency (%)
3.492750911 1
0.1%
3.479543521 1
0.1%
3.460897794 1
0.1%
3.444582783 1
0.1%
3.435259919 1
0.1%
3.422052529 1
0.1%
3.380876548 1
0.1%
3.357569389 1
0.1%
3.280655764 1
0.1%
3.272886711 1
0.1%

BusinessTravel_Non-Travel
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size11.6 KiB
0.0
1320 
1.0
150 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters4410
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 1320
89.8%
1.0 150
 
10.2%

Length

2023-05-21T21:29:37.784029image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-21T21:29:37.886698image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0.0 1320
89.8%
1.0 150
 
10.2%

Most occurring characters

ValueCountFrequency (%)
0 2790
63.3%
. 1470
33.3%
1 150
 
3.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2940
66.7%
Other Punctuation 1470
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 2790
94.9%
1 150
 
5.1%
Other Punctuation
ValueCountFrequency (%)
. 1470
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 4410
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 2790
63.3%
. 1470
33.3%
1 150
 
3.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4410
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 2790
63.3%
. 1470
33.3%
1 150
 
3.4%
Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size11.6 KiB
0.0
1193 
1.0
277 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters4410
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row1.0
3rd row0.0
4th row1.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 1193
81.2%
1.0 277
 
18.8%

Length

2023-05-21T21:29:37.968997image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-21T21:29:38.069109image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0.0 1193
81.2%
1.0 277
 
18.8%

Most occurring characters

ValueCountFrequency (%)
0 2663
60.4%
. 1470
33.3%
1 277
 
6.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2940
66.7%
Other Punctuation 1470
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 2663
90.6%
1 277
 
9.4%
Other Punctuation
ValueCountFrequency (%)
. 1470
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 4410
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 2663
60.4%
. 1470
33.3%
1 277
 
6.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4410
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 2663
60.4%
. 1470
33.3%
1 277
 
6.3%
Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size11.6 KiB
1.0
1043 
0.0
427 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters4410
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row0.0
3rd row1.0
4th row0.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 1043
71.0%
0.0 427
29.0%

Length

2023-05-21T21:29:38.156301image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-21T21:29:38.258065image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
1.0 1043
71.0%
0.0 427
29.0%

Most occurring characters

ValueCountFrequency (%)
0 1897
43.0%
. 1470
33.3%
1 1043
23.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2940
66.7%
Other Punctuation 1470
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1897
64.5%
1 1043
35.5%
Other Punctuation
ValueCountFrequency (%)
. 1470
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 4410
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1897
43.0%
. 1470
33.3%
1 1043
23.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4410
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1897
43.0%
. 1470
33.3%
1 1043
23.7%

EducationField_Human Resources
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size11.6 KiB
0.0
1443 
1.0
 
27

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters4410
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 1443
98.2%
1.0 27
 
1.8%

Length

2023-05-21T21:29:38.341323image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-21T21:29:38.441527image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0.0 1443
98.2%
1.0 27
 
1.8%

Most occurring characters

ValueCountFrequency (%)
0 2913
66.1%
. 1470
33.3%
1 27
 
0.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2940
66.7%
Other Punctuation 1470
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 2913
99.1%
1 27
 
0.9%
Other Punctuation
ValueCountFrequency (%)
. 1470
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 4410
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 2913
66.1%
. 1470
33.3%
1 27
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4410
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 2913
66.1%
. 1470
33.3%
1 27
 
0.6%
Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size11.6 KiB
0.0
864 
1.0
606 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters4410
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row0.0
4th row1.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 864
58.8%
1.0 606
41.2%

Length

2023-05-21T21:29:38.523309image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-21T21:29:38.626962image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0.0 864
58.8%
1.0 606
41.2%

Most occurring characters

ValueCountFrequency (%)
0 2334
52.9%
. 1470
33.3%
1 606
 
13.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2940
66.7%
Other Punctuation 1470
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 2334
79.4%
1 606
 
20.6%
Other Punctuation
ValueCountFrequency (%)
. 1470
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 4410
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 2334
52.9%
. 1470
33.3%
1 606
 
13.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4410
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 2334
52.9%
. 1470
33.3%
1 606
 
13.7%
Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size11.6 KiB
0.0
1311 
1.0
159 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters4410
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 1311
89.2%
1.0 159
 
10.8%

Length

2023-05-21T21:29:38.712306image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-21T21:29:38.813070image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0.0 1311
89.2%
1.0 159
 
10.8%

Most occurring characters

ValueCountFrequency (%)
0 2781
63.1%
. 1470
33.3%
1 159
 
3.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2940
66.7%
Other Punctuation 1470
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 2781
94.6%
1 159
 
5.4%
Other Punctuation
ValueCountFrequency (%)
. 1470
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 4410
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 2781
63.1%
. 1470
33.3%
1 159
 
3.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4410
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 2781
63.1%
. 1470
33.3%
1 159
 
3.6%
Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size11.6 KiB
0.0
1006 
1.0
464 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters4410
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row1.0

Common Values

ValueCountFrequency (%)
0.0 1006
68.4%
1.0 464
31.6%

Length

2023-05-21T21:29:38.897513image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-21T21:29:38.997286image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0.0 1006
68.4%
1.0 464
31.6%

Most occurring characters

ValueCountFrequency (%)
0 2476
56.1%
. 1470
33.3%
1 464
 
10.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2940
66.7%
Other Punctuation 1470
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 2476
84.2%
1 464
 
15.8%
Other Punctuation
ValueCountFrequency (%)
. 1470
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 4410
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 2476
56.1%
. 1470
33.3%
1 464
 
10.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4410
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 2476
56.1%
. 1470
33.3%
1 464
 
10.5%
Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size11.6 KiB
0.0
1388 
1.0
 
82

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters4410
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row1.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 1388
94.4%
1.0 82
 
5.6%

Length

2023-05-21T21:29:39.083451image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-21T21:29:39.181271image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0.0 1388
94.4%
1.0 82
 
5.6%

Most occurring characters

ValueCountFrequency (%)
0 2858
64.8%
. 1470
33.3%
1 82
 
1.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2940
66.7%
Other Punctuation 1470
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 2858
97.2%
1 82
 
2.8%
Other Punctuation
ValueCountFrequency (%)
. 1470
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 4410
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 2858
64.8%
. 1470
33.3%
1 82
 
1.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4410
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 2858
64.8%
. 1470
33.3%
1 82
 
1.9%
Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size11.6 KiB
0.0
1338 
1.0
 
132

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters4410
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 1338
91.0%
1.0 132
 
9.0%

Length

2023-05-21T21:29:39.263240image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-21T21:29:39.364204image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0.0 1338
91.0%
1.0 132
 
9.0%

Most occurring characters

ValueCountFrequency (%)
0 2808
63.7%
. 1470
33.3%
1 132
 
3.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2940
66.7%
Other Punctuation 1470
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 2808
95.5%
1 132
 
4.5%
Other Punctuation
ValueCountFrequency (%)
. 1470
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 4410
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 2808
63.7%
. 1470
33.3%
1 132
 
3.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4410
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 2808
63.7%
. 1470
33.3%
1 132
 
3.0%

Gender_Female
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size11.6 KiB
0.0
882 
1.0
588 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters4410
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row0.0
3rd row0.0
4th row1.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 882
60.0%
1.0 588
40.0%

Length

2023-05-21T21:29:39.446615image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-21T21:29:39.551227image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0.0 882
60.0%
1.0 588
40.0%

Most occurring characters

ValueCountFrequency (%)
0 2352
53.3%
. 1470
33.3%
1 588
 
13.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2940
66.7%
Other Punctuation 1470
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 2352
80.0%
1 588
 
20.0%
Other Punctuation
ValueCountFrequency (%)
. 1470
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 4410
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 2352
53.3%
. 1470
33.3%
1 588
 
13.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4410
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 2352
53.3%
. 1470
33.3%
1 588
 
13.3%

Gender_Male
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size11.6 KiB
1.0
882 
0.0
588 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters4410
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row1.0
3rd row1.0
4th row0.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 882
60.0%
0.0 588
40.0%

Length

2023-05-21T21:29:39.636823image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-21T21:29:39.738297image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
1.0 882
60.0%
0.0 588
40.0%

Most occurring characters

ValueCountFrequency (%)
0 2058
46.7%
. 1470
33.3%
1 882
20.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2940
66.7%
Other Punctuation 1470
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 2058
70.0%
1 882
30.0%
Other Punctuation
ValueCountFrequency (%)
. 1470
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 4410
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 2058
46.7%
. 1470
33.3%
1 882
20.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4410
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 2058
46.7%
. 1470
33.3%
1 882
20.0%
Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size11.6 KiB
0.0
1339 
1.0
 
131

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters4410
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 1339
91.1%
1.0 131
 
8.9%

Length

2023-05-21T21:29:39.822672image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-21T21:29:40.017152image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0.0 1339
91.1%
1.0 131
 
8.9%

Most occurring characters

ValueCountFrequency (%)
0 2809
63.7%
. 1470
33.3%
1 131
 
3.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2940
66.7%
Other Punctuation 1470
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 2809
95.5%
1 131
 
4.5%
Other Punctuation
ValueCountFrequency (%)
. 1470
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 4410
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 2809
63.7%
. 1470
33.3%
1 131
 
3.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4410
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 2809
63.7%
. 1470
33.3%
1 131
 
3.0%

JobRole_Human Resources
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size11.6 KiB
0.0
1418 
1.0
 
52

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters4410
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 1418
96.5%
1.0 52
 
3.5%

Length

2023-05-21T21:29:40.099901image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-21T21:29:40.198066image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0.0 1418
96.5%
1.0 52
 
3.5%

Most occurring characters

ValueCountFrequency (%)
0 2888
65.5%
. 1470
33.3%
1 52
 
1.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2940
66.7%
Other Punctuation 1470
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 2888
98.2%
1 52
 
1.8%
Other Punctuation
ValueCountFrequency (%)
. 1470
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 4410
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 2888
65.5%
. 1470
33.3%
1 52
 
1.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4410
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 2888
65.5%
. 1470
33.3%
1 52
 
1.2%
Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size11.6 KiB
0.0
1211 
1.0
259 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters4410
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row1.0
4th row0.0
5th row1.0

Common Values

ValueCountFrequency (%)
0.0 1211
82.4%
1.0 259
 
17.6%

Length

2023-05-21T21:29:40.280038image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-21T21:29:40.378567image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0.0 1211
82.4%
1.0 259
 
17.6%

Most occurring characters

ValueCountFrequency (%)
0 2681
60.8%
. 1470
33.3%
1 259
 
5.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2940
66.7%
Other Punctuation 1470
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 2681
91.2%
1 259
 
8.8%
Other Punctuation
ValueCountFrequency (%)
. 1470
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 4410
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 2681
60.8%
. 1470
33.3%
1 259
 
5.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4410
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 2681
60.8%
. 1470
33.3%
1 259
 
5.9%

JobRole_Manager
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size11.6 KiB
0.0
1368 
1.0
 
102

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters4410
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 1368
93.1%
1.0 102
 
6.9%

Length

2023-05-21T21:29:40.462193image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-21T21:29:40.562160image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0.0 1368
93.1%
1.0 102
 
6.9%

Most occurring characters

ValueCountFrequency (%)
0 2838
64.4%
. 1470
33.3%
1 102
 
2.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2940
66.7%
Other Punctuation 1470
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 2838
96.5%
1 102
 
3.5%
Other Punctuation
ValueCountFrequency (%)
. 1470
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 4410
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 2838
64.4%
. 1470
33.3%
1 102
 
2.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4410
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 2838
64.4%
. 1470
33.3%
1 102
 
2.3%
Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size11.6 KiB
0.0
1325 
1.0
145 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters4410
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 1325
90.1%
1.0 145
 
9.9%

Length

2023-05-21T21:29:40.646284image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-21T21:29:40.745598image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0.0 1325
90.1%
1.0 145
 
9.9%

Most occurring characters

ValueCountFrequency (%)
0 2795
63.4%
. 1470
33.3%
1 145
 
3.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2940
66.7%
Other Punctuation 1470
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 2795
95.1%
1 145
 
4.9%
Other Punctuation
ValueCountFrequency (%)
. 1470
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 4410
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 2795
63.4%
. 1470
33.3%
1 145
 
3.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4410
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 2795
63.4%
. 1470
33.3%
1 145
 
3.3%

JobRole_Research Director
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size11.6 KiB
0.0
1390 
1.0
 
80

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters4410
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 1390
94.6%
1.0 80
 
5.4%

Length

2023-05-21T21:29:40.831777image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-21T21:29:40.930392image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0.0 1390
94.6%
1.0 80
 
5.4%

Most occurring characters

ValueCountFrequency (%)
0 2860
64.9%
. 1470
33.3%
1 80
 
1.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2940
66.7%
Other Punctuation 1470
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 2860
97.3%
1 80
 
2.7%
Other Punctuation
ValueCountFrequency (%)
. 1470
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 4410
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 2860
64.9%
. 1470
33.3%
1 80
 
1.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4410
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 2860
64.9%
. 1470
33.3%
1 80
 
1.8%
Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size11.6 KiB
0.0
1178 
1.0
292 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters4410
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row1.0
3rd row0.0
4th row1.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 1178
80.1%
1.0 292
 
19.9%

Length

2023-05-21T21:29:41.011714image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-21T21:29:41.112933image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0.0 1178
80.1%
1.0 292
 
19.9%

Most occurring characters

ValueCountFrequency (%)
0 2648
60.0%
. 1470
33.3%
1 292
 
6.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2940
66.7%
Other Punctuation 1470
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 2648
90.1%
1 292
 
9.9%
Other Punctuation
ValueCountFrequency (%)
. 1470
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 4410
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 2648
60.0%
. 1470
33.3%
1 292
 
6.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4410
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 2648
60.0%
. 1470
33.3%
1 292
 
6.6%
Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size11.6 KiB
0.0
1144 
1.0
326 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters4410
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 1144
77.8%
1.0 326
 
22.2%

Length

2023-05-21T21:29:41.198718image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-21T21:29:41.299101image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0.0 1144
77.8%
1.0 326
 
22.2%

Most occurring characters

ValueCountFrequency (%)
0 2614
59.3%
. 1470
33.3%
1 326
 
7.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2940
66.7%
Other Punctuation 1470
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 2614
88.9%
1 326
 
11.1%
Other Punctuation
ValueCountFrequency (%)
. 1470
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 4410
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 2614
59.3%
. 1470
33.3%
1 326
 
7.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4410
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 2614
59.3%
. 1470
33.3%
1 326
 
7.4%
Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size11.6 KiB
0.0
1387 
1.0
 
83

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters4410
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 1387
94.4%
1.0 83
 
5.6%

Length

2023-05-21T21:29:41.390249image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-21T21:29:41.492512image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0.0 1387
94.4%
1.0 83
 
5.6%

Most occurring characters

ValueCountFrequency (%)
0 2857
64.8%
. 1470
33.3%
1 83
 
1.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2940
66.7%
Other Punctuation 1470
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 2857
97.2%
1 83
 
2.8%
Other Punctuation
ValueCountFrequency (%)
. 1470
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 4410
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 2857
64.8%
. 1470
33.3%
1 83
 
1.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4410
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 2857
64.8%
. 1470
33.3%
1 83
 
1.9%
Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size11.6 KiB
0.0
1143 
1.0
327 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters4410
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 1143
77.8%
1.0 327
 
22.2%

Length

2023-05-21T21:29:41.578409image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-21T21:29:41.680231image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0.0 1143
77.8%
1.0 327
 
22.2%

Most occurring characters

ValueCountFrequency (%)
0 2613
59.3%
. 1470
33.3%
1 327
 
7.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2940
66.7%
Other Punctuation 1470
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 2613
88.9%
1 327
 
11.1%
Other Punctuation
ValueCountFrequency (%)
. 1470
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 4410
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 2613
59.3%
. 1470
33.3%
1 327
 
7.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4410
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 2613
59.3%
. 1470
33.3%
1 327
 
7.4%
Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size11.6 KiB
0.0
797 
1.0
673 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters4410
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row1.0
3rd row0.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
0.0 797
54.2%
1.0 673
45.8%

Length

2023-05-21T21:29:41.767811image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-21T21:29:41.871099image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0.0 797
54.2%
1.0 673
45.8%

Most occurring characters

ValueCountFrequency (%)
0 2267
51.4%
. 1470
33.3%
1 673
 
15.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2940
66.7%
Other Punctuation 1470
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 2267
77.1%
1 673
 
22.9%
Other Punctuation
ValueCountFrequency (%)
. 1470
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 4410
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 2267
51.4%
. 1470
33.3%
1 673
 
15.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4410
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 2267
51.4%
. 1470
33.3%
1 673
 
15.3%
Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size11.6 KiB
0.0
1000 
1.0
470 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters4410
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row0.0
3rd row1.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 1000
68.0%
1.0 470
32.0%

Length

2023-05-21T21:29:41.955525image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-21T21:29:42.059641image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0.0 1000
68.0%
1.0 470
32.0%

Most occurring characters

ValueCountFrequency (%)
0 2470
56.0%
. 1470
33.3%
1 470
 
10.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2940
66.7%
Other Punctuation 1470
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 2470
84.0%
1 470
 
16.0%
Other Punctuation
ValueCountFrequency (%)
. 1470
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 4410
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 2470
56.0%
. 1470
33.3%
1 470
 
10.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4410
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 2470
56.0%
. 1470
33.3%
1 470
 
10.7%

OverTime_No
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size11.6 KiB
1.0
1054 
0.0
416 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters4410
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row1.0
3rd row0.0
4th row0.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 1054
71.7%
0.0 416
 
28.3%

Length

2023-05-21T21:29:42.143721image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-21T21:29:42.246013image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
1.0 1054
71.7%
0.0 416
 
28.3%

Most occurring characters

ValueCountFrequency (%)
0 1886
42.8%
. 1470
33.3%
1 1054
23.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2940
66.7%
Other Punctuation 1470
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1886
64.1%
1 1054
35.9%
Other Punctuation
ValueCountFrequency (%)
. 1470
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 4410
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1886
42.8%
. 1470
33.3%
1 1054
23.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4410
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1886
42.8%
. 1470
33.3%
1 1054
23.9%

OverTime_Yes
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size11.6 KiB
0.0
1054 
1.0
416 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters4410
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row0.0
3rd row1.0
4th row1.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 1054
71.7%
1.0 416
 
28.3%

Length

2023-05-21T21:29:42.328886image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-21T21:29:42.428632image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0.0 1054
71.7%
1.0 416
 
28.3%

Most occurring characters

ValueCountFrequency (%)
0 2524
57.2%
. 1470
33.3%
1 416
 
9.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2940
66.7%
Other Punctuation 1470
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 2524
85.9%
1 416
 
14.1%
Other Punctuation
ValueCountFrequency (%)
. 1470
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 4410
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 2524
57.2%
. 1470
33.3%
1 416
 
9.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4410
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 2524
57.2%
. 1470
33.3%
1 416
 
9.4%

Interactions

2023-05-21T21:29:30.108315image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-21T21:29:10.494442image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-21T21:29:12.018661image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-21T21:29:13.519622image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-21T21:29:15.064659image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-21T21:29:16.716609image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-21T21:29:18.250085image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-21T21:29:19.797107image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-21T21:29:21.315431image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-21T21:29:22.819670image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-21T21:29:24.311316image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-21T21:29:25.752017image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-21T21:29:27.272252image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-21T21:29:28.692296image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-21T21:29:30.206331image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-21T21:29:10.601995image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-21T21:29:12.118395image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-21T21:29:13.629333image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-21T21:29:15.170822image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-21T21:29:16.820117image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-21T21:29:18.352775image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-21T21:29:19.890965image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-21T21:29:21.407307image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-21T21:29:22.918928image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-21T21:29:24.407095image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-21T21:29:25.842668image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-21T21:29:27.366005image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-21T21:29:28.786306image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-21T21:29:30.313955image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-21T21:29:10.713631image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-21T21:29:12.225960image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-21T21:29:13.741030image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-21T21:29:15.283512image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-21T21:29:16.933557image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-21T21:29:18.462075image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-21T21:29:19.993118image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-21T21:29:21.511992image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-21T21:29:23.024698image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-21T21:29:24.513228image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-21T21:29:25.945395image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-21T21:29:27.467736image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-21T21:29:28.888005image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-21T21:29:30.428643image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-21T21:29:10.838336image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-21T21:29:12.337771image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-21T21:29:13.857718image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-21T21:29:15.400067image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-21T21:29:17.049664image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-21T21:29:18.578506image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-21T21:29:20.112052image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-21T21:29:21.626684image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-21T21:29:23.138927image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-21T21:29:24.626888image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-21T21:29:26.054047image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-21T21:29:27.578432image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-21T21:29:28.998458image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-21T21:29:30.548055image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-21T21:29:11.019873image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-21T21:29:12.452405image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-21T21:29:13.975619image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-21T21:29:15.519828image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-21T21:29:17.168334image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-21T21:29:18.698882image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-21T21:29:20.223791image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-21T21:29:21.742761image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-21T21:29:23.254651image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-21T21:29:24.740693image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-21T21:29:26.166176image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-21T21:29:27.694442image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-21T21:29:29.108727image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-21T21:29:30.666457image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-21T21:29:11.131132image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-21T21:29:12.565437image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-21T21:29:14.088959image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-21T21:29:15.637522image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-21T21:29:17.281997image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-21T21:29:18.814340image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-21T21:29:20.331659image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-21T21:29:21.851454image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-21T21:29:23.373201image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-21T21:29:24.851685image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-21T21:29:26.372083image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-21T21:29:27.804044image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-21T21:29:29.218434image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-21T21:29:30.779045image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-21T21:29:11.240171image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-21T21:29:12.683709image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-21T21:29:14.207642image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-21T21:29:15.846960image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-21T21:29:17.397477image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-21T21:29:18.929000image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-21T21:29:20.440367image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-21T21:29:21.960325image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-21T21:29:23.502349image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-21T21:29:24.961390image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-21T21:29:26.486175image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-21T21:29:27.913805image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-21T21:29:29.326421image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-21T21:29:30.881181image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-21T21:29:11.334993image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-21T21:29:12.784188image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-21T21:29:14.309604image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-21T21:29:15.951686image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-21T21:29:17.499468image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-21T21:29:19.031452image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-21T21:29:20.540775image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-21T21:29:22.059096image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-21T21:29:23.604878image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-21T21:29:25.059137image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-21T21:29:26.588836image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-21T21:29:28.005792image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-21T21:29:29.422328image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-21T21:29:30.982875image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-21T21:29:11.427716image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-21T21:29:12.891567image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-21T21:29:14.419309image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-21T21:29:16.057416image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-21T21:29:17.607215image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-21T21:29:19.135561image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-21T21:29:20.633564image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-21T21:29:22.227644image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-21T21:29:23.703607image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-21T21:29:25.153342image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-21T21:29:26.685609image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-21T21:29:28.101040image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-21T21:29:29.517390image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-21T21:29:31.092582image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-21T21:29:11.529954image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-21T21:29:13.003335image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-21T21:29:14.530335image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-21T21:29:16.169151image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-21T21:29:17.717278image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-21T21:29:19.250674image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-21T21:29:20.736762image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-21T21:29:22.331389image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-21T21:29:23.810840image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-21T21:29:25.256142image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-21T21:29:26.787341image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-21T21:29:28.206361image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-21T21:29:29.621132image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-21T21:29:31.198300image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-21T21:29:11.628717image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-21T21:29:13.109854image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-21T21:29:14.640116image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-21T21:29:16.277280image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-21T21:29:17.826975image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-21T21:29:19.374344image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-21T21:29:20.932450image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-21T21:29:22.431103image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-21T21:29:23.911901image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-21T21:29:25.355871image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-21T21:29:26.886039image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-21T21:29:28.306527image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-21T21:29:29.721841image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-21T21:29:31.313989image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-21T21:29:11.727290image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-21T21:29:13.210579image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-21T21:29:14.744961image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-21T21:29:16.385769image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-21T21:29:17.930992image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-21T21:29:19.477005image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-21T21:29:21.027578image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-21T21:29:22.524430image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-21T21:29:24.011670image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-21T21:29:25.451404image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-21T21:29:26.980087image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-21T21:29:28.400504image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-21T21:29:29.817139image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-21T21:29:31.414568image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-21T21:29:11.822998image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-21T21:29:13.311682image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-21T21:29:14.849680image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-21T21:29:16.492459image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-21T21:29:18.034841image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-21T21:29:19.579734image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-21T21:29:21.120331image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-21T21:29:22.621470image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-21T21:29:24.108412image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-21T21:29:25.549238image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-21T21:29:27.075591image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-21T21:29:28.493253image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-21T21:29:29.911853image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-21T21:29:31.515412image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-21T21:29:11.916216image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-21T21:29:13.410913image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-21T21:29:14.953403image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-21T21:29:16.603162image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-21T21:29:18.138418image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-21T21:29:19.686811image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-21T21:29:21.214734image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-21T21:29:22.715998image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-21T21:29:24.208149image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-21T21:29:25.644982image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-21T21:29:27.170524image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-21T21:29:28.589997image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-21T21:29:30.005082image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Correlations

2023-05-21T21:29:42.569257image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
AgeDailyRateDistanceFromHomeMonthlyIncomeNumCompaniesWorkedPercentSalaryHikeTotalWorkingYearsTrainingTimesLastYearYearsAtCompanyYearsInCurrentRoleYearsSinceLastPromotionYearsWithCurrManagerAverageSatisfactionSalaryDeviationEducationEnvironmentSatisfactionJobInvolvementJobLevelJobSatisfactionRelationshipSatisfactionStockOptionLevelWorkLifeBalanceBusinessTravel_Non-TravelBusinessTravel_Travel_FrequentlyBusinessTravel_Travel_RarelyEducationField_Human ResourcesEducationField_Life SciencesEducationField_MarketingEducationField_MedicalEducationField_OtherEducationField_Technical DegreeGender_FemaleGender_MaleJobRole_Healthcare RepresentativeJobRole_Human ResourcesJobRole_Laboratory TechnicianJobRole_ManagerJobRole_Manufacturing DirectorJobRole_Research DirectorJobRole_Research ScientistJobRole_Sales ExecutiveJobRole_Sales RepresentativeMaritalStatus_DivorcedMaritalStatus_MarriedMaritalStatus_SingleOverTime_NoOverTime_Yes
Age1.0000.007-0.0190.4720.3530.0080.6570.0000.2520.1980.1740.1950.0230.0730.1530.0060.0250.2950.0000.0350.0930.0330.0000.0580.0480.0000.0270.0000.0000.0420.0330.0000.0000.1060.0360.1400.3130.0910.2000.1650.1120.2280.0940.1130.1960.0000.000
DailyRate0.0071.000-0.0030.0160.0370.0250.021-0.011-0.0100.007-0.038-0.0050.0340.0200.0170.0000.0160.0000.0000.0000.0400.0120.0230.0390.0000.0120.0330.0620.0140.0000.0860.0310.0310.0420.0000.0350.0000.0000.0360.0000.0000.0000.0590.1080.0880.0000.000
DistanceFromHome-0.019-0.0031.0000.003-0.0100.030-0.003-0.0250.0110.014-0.0050.004-0.014-0.0540.0000.0000.0340.0540.0000.0190.0000.0000.0000.0410.0510.0380.0000.0000.0000.0000.0000.0260.0260.0000.0000.0000.0410.0000.0000.0000.0320.0220.0000.0000.0000.0640.064
MonthlyIncome0.4720.0160.0031.0000.190-0.0340.710-0.0350.4640.3950.2650.365-0.0090.3440.0940.0000.0460.8640.0000.0430.0560.0000.0000.0470.0380.0640.0000.1500.0160.0550.0410.0460.0460.2940.0780.3790.7270.2720.5460.4050.4620.2910.0490.0440.0810.0000.000
NumCompaniesWorked0.3530.037-0.0100.1901.0000.0000.315-0.047-0.171-0.128-0.067-0.144-0.0050.1000.1010.0000.0000.1130.0000.0000.0000.0510.0000.0000.0000.0630.0910.0720.0550.0530.0130.0000.0000.0710.0660.0680.0850.0380.1200.0710.0420.1060.0250.0260.0550.0000.000
PercentSalaryHike0.0080.0250.030-0.0340.0001.000-0.026-0.004-0.054-0.026-0.055-0.026-0.0250.0120.0210.0000.0360.0000.0000.0270.0000.0000.0280.0030.0600.0000.0000.0000.0000.0000.0320.0490.0490.0080.0000.0000.0680.0000.0000.0410.0000.0370.0000.0000.0000.0000.000
TotalWorkingYears0.6570.021-0.0030.7100.315-0.0261.000-0.0140.5940.4930.3350.495-0.0190.1150.0950.0000.0000.5390.0240.0310.0640.0000.0000.0170.0000.0000.0000.0590.0660.0390.0000.0000.0000.1770.0390.2150.5720.1030.3300.2560.2130.3390.0000.0650.1130.0000.000
TrainingTimesLastYear0.000-0.011-0.025-0.035-0.047-0.004-0.0141.0000.0010.0050.010-0.012-0.015-0.0110.0270.0000.0130.0170.0210.0000.0000.0000.0000.0000.0000.0000.0000.0550.0940.0260.0450.0000.0000.0000.0000.0000.0000.0330.0000.0390.0000.0500.0000.0320.0000.0990.099
YearsAtCompany0.252-0.0100.0110.464-0.171-0.0540.5940.0011.0000.8540.5200.8430.0080.0570.0680.0000.0390.3470.0000.0000.0150.0000.0000.0000.0320.0000.0470.0000.0000.0000.0000.0330.0330.0680.0000.1320.3970.0660.1740.1340.1540.2130.0660.0630.0310.0450.045
YearsInCurrentRole0.1980.0070.0140.395-0.128-0.0260.4930.0050.8541.0000.5060.725-0.0050.0680.0390.0320.0000.2410.0000.0000.0000.0240.0060.0000.0000.0000.0260.0510.0000.0000.0000.0800.0800.0890.0000.1200.1970.0590.1820.1480.1270.1440.0000.0540.0780.0540.054
YearsSinceLastPromotion0.174-0.038-0.0050.265-0.067-0.0550.3350.0100.5200.5061.0000.4670.0320.0520.0000.0000.0000.2120.0000.0520.0530.0130.0000.0360.0000.0000.0000.0000.0000.0000.0000.0000.0000.0830.0000.0880.2420.0620.1280.1200.0620.0640.0370.0880.0460.0000.000
YearsWithCurrManager0.195-0.0050.0040.365-0.144-0.0260.495-0.0120.8430.7250.4671.000-0.0200.0410.0000.0000.0440.2320.0000.0000.0300.0310.0000.0890.0900.0000.0000.0290.0000.0000.0000.0000.0000.0000.0000.0890.2070.0720.1530.1190.0890.1550.0000.0000.0480.0000.000
AverageSatisfaction0.0230.034-0.014-0.009-0.005-0.025-0.019-0.0150.008-0.0050.032-0.0201.000-0.0310.0130.3660.0510.0440.3540.3610.0620.0180.0000.0000.0210.0000.0500.0000.0570.0000.0490.0150.0150.0000.0290.0000.0000.0000.0000.0000.0090.0000.0000.0380.0730.1130.113
SalaryDeviation0.0730.020-0.0540.3440.1000.0120.115-0.0110.0570.0680.0520.041-0.0311.0000.0100.0390.0000.2850.0000.0480.0360.0000.0370.0000.0460.0000.0000.0470.0000.0000.0000.0000.0000.1540.0000.1410.2020.1360.2880.1850.1550.0930.0000.0260.0640.0000.000
Education0.1530.0170.0000.0940.1010.0210.0950.0270.0680.0390.0000.0000.0130.0101.0000.0190.0000.0880.0150.0160.0270.0000.0200.0000.0000.0490.0000.0640.0610.0720.0000.0000.0000.0000.0000.0430.0000.0000.0580.0000.0470.0940.0000.0000.0000.0010.001
EnvironmentSatisfaction0.0060.0000.0000.0000.0000.0000.0000.0000.0000.0320.0000.0000.3660.0390.0191.0000.0340.0000.0000.0000.0000.0000.0100.0000.0000.0290.0170.0000.0410.0500.0000.0000.0000.0000.0240.0000.0000.0390.0310.0000.0000.0320.0000.0510.0180.0600.060
JobInvolvement0.0250.0160.0340.0460.0000.0360.0000.0130.0390.0000.0000.0440.0510.0000.0000.0341.0000.0000.0000.0000.0220.0000.0460.0000.0130.0180.0000.0290.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0280.0000.0000.0220.0150.0300.0000.000
JobLevel0.2950.0000.0540.8640.1130.0000.5390.0170.3470.2410.2120.2320.0440.2850.0880.0000.0001.0000.0000.0000.0690.0000.0370.0000.0310.0640.0000.1720.0350.0000.0690.0480.0480.2750.1010.3960.6570.2870.4490.4540.4830.2730.0000.0280.0780.0000.000
JobSatisfaction0.0000.0000.0000.0000.0000.0000.0240.0210.0000.0000.0000.0000.3540.0000.0150.0000.0000.0001.0000.0000.0000.0000.0220.0000.0020.0000.0440.0550.0000.0000.0000.0000.0000.0000.0370.0000.0000.0060.0000.0000.0230.0190.0000.0000.0000.0220.022
RelationshipSatisfaction0.0350.0000.0190.0430.0000.0270.0310.0000.0000.0000.0520.0000.3610.0480.0160.0000.0000.0000.0001.0000.0300.0000.0000.0000.0000.0260.0000.0670.0370.0000.0390.0000.0000.0000.0210.0590.0000.0290.0000.0260.0470.0000.0330.0250.0080.0250.025
StockOptionLevel0.0930.0400.0000.0560.0000.0000.0640.0000.0150.0000.0530.0300.0620.0360.0270.0000.0220.0690.0000.0301.0000.0190.0000.0000.0000.0550.0000.0000.0530.0000.0000.0000.0000.0000.0000.0320.0800.0000.0000.0210.0180.0250.4630.3950.7900.0000.000
WorkLifeBalance0.0330.0120.0000.0000.0510.0000.0000.0000.0000.0240.0130.0310.0180.0000.0000.0000.0000.0000.0000.0000.0191.0000.0000.0000.0000.0270.0430.0090.0180.0050.0080.0000.0000.0000.0570.0150.0000.0000.0000.0600.0000.0320.0280.0000.0000.0000.000
BusinessTravel_Non-Travel0.0000.0230.0000.0000.0000.0280.0000.0000.0000.0060.0000.0000.0000.0370.0200.0100.0460.0370.0220.0000.0000.0001.0000.1570.5240.0000.0000.0070.0000.0000.0000.0410.0410.0000.0000.0000.0000.0000.0000.0000.0110.0120.0480.0320.0000.0230.023
BusinessTravel_Travel_Frequently0.0580.0390.0410.0470.0000.0030.0170.0000.0000.0000.0360.0890.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.1571.0000.7510.0000.0130.0000.0000.0000.0000.0000.0000.0000.0000.0000.0290.0000.0000.0000.0000.0450.0000.0130.0000.0090.009
BusinessTravel_Travel_Rarely0.0480.0000.0510.0380.0000.0600.0000.0000.0320.0000.0000.0900.0210.0460.0000.0000.0130.0310.0020.0000.0000.0000.5240.7511.0000.0000.0120.0190.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0170.0000.0000.0000.0320.0470.0000.0000.000
EducationField_Human Resources0.0000.0120.0380.0640.0630.0000.0000.0000.0000.0000.0000.0000.0000.0000.0490.0290.0180.0640.0000.0260.0550.0270.0000.0000.0001.0000.1060.0300.0830.0000.0220.0000.0000.0220.5360.0500.0670.0260.0000.0560.0620.0000.0000.0450.0610.0000.000
EducationField_Life Sciences0.0270.0330.0000.0000.0910.0000.0000.0000.0470.0260.0000.0000.0500.0000.0000.0170.0000.0000.0440.0000.0000.0430.0000.0130.0120.1061.0000.2880.5670.1990.2590.0000.0000.0050.0530.0340.0000.0420.0000.0330.0860.0310.0000.0000.0000.0000.000
EducationField_Marketing0.0000.0620.0000.1500.0720.0000.0590.0550.0000.0510.0000.0290.0000.0470.0640.0000.0290.1720.0550.0670.0000.0090.0070.0000.0190.0300.2881.0000.2330.0760.1020.0000.0000.1020.0550.1560.0000.1080.0740.1690.4540.1260.0000.0000.0000.0000.000
EducationField_Medical0.0000.0140.0000.0160.0550.0000.0660.0940.0000.0000.0000.0000.0570.0000.0610.0410.0000.0350.0000.0370.0530.0180.0000.0000.0000.0830.5670.2331.0000.1600.2090.0000.0000.0180.0290.0590.0000.0200.0540.0280.1290.0410.0000.0000.0000.0000.000
EducationField_Other0.0420.0000.0000.0550.0530.0000.0390.0260.0000.0000.0000.0000.0000.0000.0720.0500.0000.0000.0000.0000.0000.0050.0000.0000.0000.0000.1990.0760.1601.0000.0660.0000.0000.0000.0000.0480.0000.0000.0000.0000.0210.0080.0000.0000.0000.0000.000
EducationField_Technical Degree0.0330.0860.0000.0410.0130.0320.0000.0450.0000.0000.0000.0000.0490.0000.0000.0000.0000.0690.0000.0390.0000.0080.0000.0000.0000.0220.2590.1020.2090.0661.0000.0000.0000.0000.0000.0000.0220.0000.0000.0680.0500.0450.0000.0000.0000.0000.000
Gender_Female0.0000.0310.0260.0460.0000.0490.0000.0000.0330.0800.0000.0000.0150.0000.0000.0000.0000.0480.0000.0000.0000.0000.0410.0000.0000.0000.0000.0000.0000.0000.0001.0000.9990.0000.0190.0610.0170.0570.0000.0000.0000.0000.0360.0000.0170.0310.031
Gender_Male0.0000.0310.0260.0460.0000.0490.0000.0000.0330.0800.0000.0000.0150.0000.0000.0000.0000.0480.0000.0000.0000.0000.0410.0000.0000.0000.0000.0000.0000.0000.0000.9991.0000.0000.0190.0610.0170.0570.0000.0000.0000.0000.0360.0000.0170.0310.031
JobRole_Healthcare Representative0.1060.0420.0000.2940.0710.0080.1770.0000.0680.0890.0830.0000.0000.1540.0000.0000.0000.2750.0000.0000.0000.0000.0000.0000.0000.0220.0050.1020.0180.0000.0000.0000.0001.0000.0470.1390.0760.0960.0650.1510.1620.0660.0000.0000.0090.0000.000
JobRole_Human Resources0.0360.0000.0000.0780.0660.0000.0390.0000.0000.0000.0000.0000.0290.0000.0000.0240.0000.1010.0370.0210.0000.0570.0000.0000.0000.5360.0530.0550.0290.0000.0000.0190.0190.0471.0000.0800.0370.0510.0270.0870.0940.0290.0000.0080.0410.0000.000
JobRole_Laboratory Technician0.1400.0350.0000.3790.0680.0000.2150.0000.1320.1200.0880.0890.0000.1410.0430.0000.0000.3960.0000.0590.0320.0150.0000.0000.0000.0500.0340.1560.0590.0480.0000.0610.0610.1390.0801.0000.1200.1480.1040.2270.2430.1060.0000.0000.0000.0340.034
JobRole_Manager0.3130.0000.0410.7270.0850.0680.5720.0000.3970.1970.2420.2070.0000.2020.0000.0000.0000.6570.0000.0000.0800.0000.0000.0290.0000.0670.0000.0000.0000.0000.0220.0170.0170.0760.0370.1201.0000.0820.0540.1300.1400.0550.0000.0390.0450.0000.000
JobRole_Manufacturing Director0.0910.0000.0000.2720.0380.0000.1030.0330.0660.0590.0620.0720.0000.1360.0000.0390.0000.2870.0060.0290.0000.0000.0000.0000.0000.0260.0420.1080.0200.0000.0000.0570.0570.0960.0510.1480.0821.0000.0700.1600.1720.0710.0000.0000.0000.0000.000
JobRole_Research Director0.2000.0360.0000.5460.1200.0000.3300.0000.1740.1820.1280.1530.0000.2880.0580.0310.0000.4490.0000.0000.0000.0000.0000.0000.0170.0000.0000.0740.0540.0000.0000.0000.0000.0650.0270.1040.0540.0701.0000.1130.1220.0450.0220.0000.0290.0000.000
JobRole_Research Scientist0.1650.0000.0000.4050.0710.0410.2560.0390.1340.1480.1200.1190.0000.1850.0000.0000.0280.4540.0000.0260.0210.0600.0000.0000.0000.0560.0330.1690.0280.0000.0680.0000.0000.1510.0870.2270.1300.1600.1131.0000.2630.1150.0000.0280.0450.0460.046
JobRole_Sales Executive0.1120.0000.0320.4620.0420.0000.2130.0000.1540.1270.0620.0890.0090.1550.0470.0000.0000.4830.0230.0470.0180.0000.0110.0000.0000.0620.0860.4540.1290.0210.0500.0000.0000.1620.0940.2430.1400.1720.1220.2631.0000.1240.0000.0000.0000.0000.000
JobRole_Sales Representative0.2280.0000.0220.2910.1060.0370.3390.0500.2130.1440.0640.1550.0000.0930.0940.0320.0000.2730.0190.0000.0250.0320.0120.0450.0000.0000.0310.1260.0410.0080.0450.0000.0000.0660.0290.1060.0550.0710.0450.1150.1241.0000.0420.0000.0640.0000.000
MaritalStatus_Divorced0.0940.0590.0000.0490.0250.0000.0000.0000.0660.0000.0370.0000.0000.0000.0000.0000.0220.0000.0000.0330.4630.0280.0480.0000.0320.0000.0000.0000.0000.0000.0000.0360.0360.0000.0000.0000.0000.0000.0220.0000.0000.0421.0000.4890.3640.0000.000
MaritalStatus_Married0.1130.1080.0000.0440.0260.0000.0650.0320.0630.0540.0880.0000.0380.0260.0000.0510.0150.0280.0000.0250.3950.0000.0320.0130.0470.0450.0000.0000.0000.0000.0000.0000.0000.0000.0080.0000.0390.0000.0000.0280.0000.0000.4891.0000.6280.0000.000
MaritalStatus_Single0.1960.0880.0000.0810.0550.0000.1130.0000.0310.0780.0460.0480.0730.0640.0000.0180.0300.0780.0000.0080.7900.0000.0000.0000.0000.0610.0000.0000.0000.0000.0000.0170.0170.0090.0410.0000.0450.0000.0290.0450.0000.0640.3640.6281.0000.0000.000
OverTime_No0.0000.0000.0640.0000.0000.0000.0000.0990.0450.0540.0000.0000.1130.0000.0010.0600.0000.0000.0220.0250.0000.0000.0230.0090.0000.0000.0000.0000.0000.0000.0000.0310.0310.0000.0000.0340.0000.0000.0000.0460.0000.0000.0000.0000.0001.0000.998
OverTime_Yes0.0000.0000.0640.0000.0000.0000.0000.0990.0450.0540.0000.0000.1130.0000.0010.0600.0000.0000.0220.0250.0000.0000.0230.0090.0000.0000.0000.0000.0000.0000.0000.0310.0310.0000.0000.0340.0000.0000.0000.0460.0000.0000.0000.0000.0000.9981.000

Missing values

2023-05-21T21:29:31.857234image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
A simple visualization of nullity by column.
2023-05-21T21:29:32.522316image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

AgeDailyRateDistanceFromHomeEducationEnvironmentSatisfactionJobInvolvementJobLevelJobSatisfactionMonthlyIncomeNumCompaniesWorkedPercentSalaryHikeRelationshipSatisfactionStockOptionLevelTotalWorkingYearsTrainingTimesLastYearWorkLifeBalanceYearsAtCompanyYearsInCurrentRoleYearsSinceLastPromotionYearsWithCurrManagerAverageSatisfactionSalaryDeviationBusinessTravel_Non-TravelBusinessTravel_Travel_FrequentlyBusinessTravel_Travel_RarelyEducationField_Human ResourcesEducationField_Life SciencesEducationField_MarketingEducationField_MedicalEducationField_OtherEducationField_Technical DegreeGender_FemaleGender_MaleJobRole_Healthcare RepresentativeJobRole_Human ResourcesJobRole_Laboratory TechnicianJobRole_ManagerJobRole_Manufacturing DirectorJobRole_Research DirectorJobRole_Research ScientistJobRole_Sales ExecutiveJobRole_Sales RepresentativeMaritalStatus_DivorcedMaritalStatus_MarriedMaritalStatus_SingleOverTime_NoOverTime_Yes
00.4463500.742527-1.010909-0.891688-0.6605310.379672-0.0577881.153254-0.1083502.125136-1.150554-1.584178-0.932014-0.421642-2.171982-2.493820-0.164613-0.063296-0.6791460.245834-0.6170570.3812450.00.01.00.01.00.00.00.00.01.00.00.00.00.00.00.00.00.01.00.00.00.01.00.01.0
11.322365-1.297775-0.147150-1.8684260.254625-1.026167-0.057788-0.660853-0.291719-0.6780492.1293061.1914380.241988-0.1645110.1557070.3380960.4885080.764998-0.3687150.8065410.444469-0.2892240.01.00.00.01.00.00.00.00.00.01.00.00.00.00.00.00.01.00.00.00.01.00.01.00.0
20.0083431.414363-0.887515-0.8916881.169781-1.026167-0.9614860.246200-0.9376541.324226-0.057267-0.658973-0.932014-0.5502080.1557070.338096-1.144294-1.167687-0.679146-1.1559350.444469-0.5414370.00.01.00.00.00.00.01.00.00.01.00.00.01.00.00.00.00.00.00.00.00.01.00.01.0
3-0.4296641.461466-0.7641211.0617871.1697810.379672-0.9614860.246200-0.763634-0.678049-1.1505540.266233-0.932014-0.4216420.1557070.3380960.1619470.7649980.252146-1.1559350.9752320.0948480.01.00.00.01.00.00.00.00.01.00.00.00.00.00.00.00.01.00.00.00.01.00.00.01.0
4-1.086676-0.524295-0.887515-1.868426-1.5756860.379672-0.961486-0.660853-0.6448582.525591-0.8772321.1914380.241988-0.6787740.1557070.338096-0.817734-0.615492-0.058285-0.595227-0.6170570.5291380.00.01.00.00.00.01.00.00.00.01.00.00.01.00.00.00.00.00.00.00.01.00.01.00.0
5-0.5391660.502054-0.887515-0.8916881.1697810.379672-0.9614861.153254-0.729850-1.078504-0.6039110.266233-0.932014-0.421642-0.620189-1.077862-0.0013330.7649980.2521460.5261881.5059950.2183760.01.00.00.01.00.00.00.00.00.01.00.00.01.00.00.00.00.00.00.00.00.01.01.00.0
62.4173841.292887-0.7641210.0850490.2546251.785511-0.961486-1.567907-0.8144160.5233161.309341-1.5841782.5899940.0926200.155707-1.077862-0.981014-1.167687-0.679146-1.155935-1.678583-0.0908320.00.01.00.00.00.01.00.00.01.00.00.00.01.00.00.00.00.00.00.00.01.00.00.01.0
7-0.7581701.3771771.827158-1.8684261.1697810.379672-0.9614860.246200-0.809529-0.6780491.855984-0.6589730.241988-1.321601-0.6201890.338096-0.981014-1.167687-0.679146-1.1559350.444469-0.0729630.00.01.00.01.00.00.00.00.00.01.00.00.01.00.00.00.00.00.00.01.00.00.01.00.0
80.117845-1.4539581.7037640.0850491.169781-1.0261670.8459110.2462000.642338-1.0785041.582663-0.658973-0.932014-0.164511-0.6201890.3380960.3252280.764998-0.3687151.0868950.444469-0.2262750.01.00.00.01.00.00.00.00.00.01.00.00.00.00.01.00.00.00.00.00.00.01.01.00.0
9-0.1011591.2309102.1973410.0850490.2546250.379672-0.0577880.246200-0.2689831.324226-0.603911-0.6589731.4159910.7354470.155707-1.077862-0.0013330.7649981.4938670.806541-0.086294-0.2060950.00.01.00.00.00.01.00.00.00.01.01.00.00.00.00.00.00.00.00.00.01.00.01.00.0
AgeDailyRateDistanceFromHomeEducationEnvironmentSatisfactionJobInvolvementJobLevelJobSatisfactionMonthlyIncomeNumCompaniesWorkedPercentSalaryHikeRelationshipSatisfactionStockOptionLevelTotalWorkingYearsTrainingTimesLastYearWorkLifeBalanceYearsAtCompanyYearsInCurrentRoleYearsSinceLastPromotionYearsWithCurrManagerAverageSatisfactionSalaryDeviationBusinessTravel_Non-TravelBusinessTravel_Travel_FrequentlyBusinessTravel_Travel_RarelyEducationField_Human ResourcesEducationField_Life SciencesEducationField_MarketingEducationField_MedicalEducationField_OtherEducationField_Technical DegreeGender_FemaleGender_MaleJobRole_Healthcare RepresentativeJobRole_Human ResourcesJobRole_Laboratory TechnicianJobRole_ManagerJobRole_Manufacturing DirectorJobRole_Research DirectorJobRole_Research ScientistJobRole_Sales ExecutiveJobRole_Sales RepresentativeMaritalStatus_DivorcedMaritalStatus_MarriedMaritalStatus_SingleOverTime_NoOverTime_Yes
1460-0.867672-0.8292242.3207351.0617871.169781-1.026167-0.961486-1.567907-0.577502-0.678049-0.330589-0.658973-0.932014-0.8073390.155707-2.493820-0.327893-0.063296-0.679146-0.034520-0.6170570.7754170.00.01.00.00.00.01.00.00.01.00.00.00.00.00.00.00.01.00.00.00.00.01.01.00.0
14611.431867-0.9730122.3207350.0850491.169781-1.0261670.845911-1.5679070.9245090.523316-0.603911-0.6589730.2419881.1211440.1557070.338096-0.654454-0.615492-0.058285-1.155935-0.6170570.8054550.00.01.00.00.01.00.00.00.00.01.00.00.00.00.00.00.00.01.00.01.00.00.00.01.0
14620.227347-0.1995321.827158-1.868426-0.660531-1.0261671.7496101.1532541.174597-1.078504-1.150554-1.5841780.2419881.249709-0.620189-1.0778622.1213101.3171932.1147280.526188-0.617057-2.6980240.00.01.00.00.01.00.00.00.01.00.00.00.00.00.00.00.00.01.00.00.01.00.01.00.0
1463-0.648668-1.183736-0.5173320.085049-0.6605310.379672-0.057788-1.5679070.729454-1.0785041.036019-0.658973-0.932014-0.164511-0.6201890.3380960.325228-0.063296-0.3687150.806541-1.6785833.4445831.00.00.00.00.00.01.00.00.00.01.00.00.00.00.01.00.00.00.00.00.00.01.01.00.0
1464-1.1961770.903668-0.5173320.0850491.169781-1.026167-0.9614860.246200-0.751522-1.0785040.7626981.191438-0.932014-0.807339-0.6201890.338096-0.491174-0.615492-0.679146-1.1559351.5059950.1391320.00.01.00.00.00.00.01.00.01.00.00.00.00.00.00.00.00.00.01.00.00.01.01.00.0
1465-0.1011590.2020821.703764-0.8916880.2546251.785511-0.0577881.153254-0.8354510.5233160.4893760.2662330.2419880.7354470.1557070.338096-0.327893-0.615492-0.679146-0.3148730.975232-2.2773250.01.00.00.00.00.01.00.00.00.01.00.00.01.00.00.00.00.00.00.00.01.00.01.00.0
14660.227347-0.469754-0.393938-1.8684261.169781-1.0261670.845911-1.5679070.7411400.523316-0.057267-1.5841780.241988-0.2930771.7075000.338096-0.0013330.764998-0.3687150.806541-1.1478200.1349860.00.01.00.00.00.01.00.00.00.01.01.00.00.00.00.00.00.00.00.00.01.00.01.00.0
1467-1.086676-1.605183-0.6407270.085049-0.6605311.785511-0.057788-0.660853-0.076690-0.6780491.309341-0.6589730.241988-0.678774-2.1719820.338096-0.164613-0.615492-0.679146-0.314873-1.1478200.4970040.00.01.00.01.00.00.00.00.00.01.00.00.00.00.01.00.00.00.00.00.01.00.00.01.0
14681.3223650.546677-0.8875150.0850491.169781-1.026167-0.057788-0.660853-0.236474-0.277594-0.3305891.191438-0.9320140.7354470.155707-1.0778620.3252280.488900-0.6791461.0868950.975232-0.0872290.01.00.00.00.00.01.00.00.00.01.00.00.00.00.00.00.00.01.00.00.01.00.01.00.0
1469-0.320163-0.432568-0.1471500.085049-0.6605311.785511-0.0577880.246200-0.445978-0.277594-0.877232-1.584178-0.932014-0.6787740.1557071.754054-0.491174-0.339394-0.368715-0.595227-1.147820-0.8532570.00.01.00.00.00.01.00.00.00.01.00.00.01.00.00.00.00.00.00.00.01.00.01.00.0